"A child's learning is the function more of the characteristics of his classmates than those of the teacher." James Coleman, 1972

Tuesday, August 23, 2016

Severe media bias persists

Groundhog Day: A comment on “Americans like their schools just fine - but not yours.”
The headline of this article should have been “Severe media bias on education persists.”
As is the case every year, the Gallup poll found that people rate their local schools much more positively than they do schools in the US in general.The differences, as usual, were striking: Seventy-six percent of parents said they would give the public schools their oldest child attended a grade or A or B, but only 25% would give public schools in the nation an A or B.
Lorraine McDonnell, a professor of political science at the University of California, Santa Barbara, has an explanation: Parents have direct information about the school their children attend, but their opinion of American education comes from the media. The image of public schools, she notes,  “is somewhat vague and increasingly negative though media images."In reality, American schools are doing quite well: When researchers control for the effects of poverty, American students' international test scores rank near the top of the world. This is strong evidence of media bias.  When parents rate the nation’s schools and their children’s school the same, we will know this bias is gone.

Stephen Krashen
http://www.npr.org/sections/ed/2016/08/23/490380129/americans-like-their-schools-just-fine-but-not-yours
This comment posted on nprEd Facebook page: https://www.facebook.com/pages/nprEd/402441646563033

Understanding KIPP Model Charter Schools: Part 6

Part 5 is here (earlier excerpts can be accessed by googling the title above).

If you are a new teacher in a "no excuses" school, you may be discovering a level of Hell that you previously did not know existed.  In the excerpt below from my book, former teachers and teachers begin to recount some of their experiences in KIPP Model schools.  I would say, enjoy, but that is not the right admonition.  Rather, read a bit, and go outside and look at the sky, then continue.


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Chapter 6
The KIPP Teaching Experience
It’s like you’re being used up and thrown out.  –1160
. . . looking back on it, it just seems crazy that people are willing to do the things that KIPP requires of them without a second thought. You know, you have to have blind obedience, if that makes sense. –1184
         As noted elsewhere in this book, KIPP teachers are at-will employees, which means that the teacher or KIPP can terminate the contract at any time without notice.  While this arrangement provides a tangible motivator for teachers to try to keep up with KIPP’s expectations, it also allows KIPP to quickly replace teachers who are not producing measurable results in the form of test scores. 
This arrangement, in turn, creates higher stress levels among teachers who are already under intense pressure, and the ability to fire teachers at-will creates a negative energy that runs counter to KIPP’s advertised face of positivity and widespread “joy factor.”  Teachers report that job insecurity was a major stressor, which creates negative energy from knowing that termination might come without warning:  “I think that KIPP is kind of fueled by negative energy in some ways. You know, it’s easier to try to scare people into doing things than to motivate them or encourage them. And I think that was kind of the attitude that I ran into a lot is, you know, you’re an at-will employee, so if you don’t like it, you can go somewhere else.”
Another teacher noted that his lowest points at KIPP were related to his anxiety about job insecurity, which caused him to lose sleep:  “. . . a part of the culture among staff at this particular school, and I think at most KIPP schools, because there’s no contract, there’s this idea of you could go at any time.  You could not be re-hired next year.  You could be let go with absolutely no notice.  I felt that pressure a lot, to the point where I was losing sleep.”
         Another teacher who had worked in public schools before coming to KIPP talked about the downside of KIPP’s system of at-will teacher contracts:
. . . public schools aren’t all perfect. . . . I’m not a wholesale believer in everything that the union has done, because I’ve seen a lot of ineffective teachers get tenure and things like that.  And I’ve seen a lot of teachers not be protected when they should have been. But I believe that there is a reason for the protections that teachers have. And at KIPP, there is no such thing. You know, you're an at-will employee all the time [and] there’s no guarantee that, if you’ve been there for five years, that year six, they’re going to even ask you to come back. And they don’t have to give you any notice.

The Teaching Day
         Teachers in KIPP Model schools have long days that range from 10 to 14 hours.  It is not uncommon for teachers to arrive at school between 5 and 6 AM and to leave after 6 or 7 PM, and all KIPP teachers are on call Monday through Friday until 9 or 10 PM for homework tutoring or questions.  Some KIPP schools have Saturday school for a half-day, but most have Saturday school every other week. “And then,” as one teacher said, “there’s the lesson planning and grading” to be done as well. 
Most teachers arrive well before 7:00, in order to get their photocopies done before the children begin to arrive at 7:05.  School leaders make the rounds checking to see if rooms are ready.  At 7:20, children are signaled with a series of hand motions to line up for breakfast, and they are marched silently in single file to the cafeteria.  Children return to the classroom for “advisory” at 7:50, where they find the morning work on the board or they are handed a worksheet that may or may not be relevant to the curriculum. 
During this time of silent work, teachers check homework folders to make sure all homework has been completed correctly and that the proper paycheck deductions for sloppy or missing work and credits for correct work are made for each child.  With as many as 30 students in advisory with four subjects, each requiring written work every night, this task rarely gets completed with complete accuracy in the time allotted.  At 8:40, children get the readying hand signals once more and are silently marched to their first class. 
One teacher explained the students’ transitioning in this way:
They would move into transition, which was always a stressful time.  It’s mean to be very routinized.  They have a thing called one, two, three dismissal, where they’re given about 20 seconds to pack up.  Not about 20 seconds; it is an exact 20 seconds.  Give them 20 seconds to pack, then they have one, two, three dismissal, where the teacher raises one finger and that indicates that all students should be tracking them. 
When all students are tracking the teacher, the teacher raises the second finger, which shows that all the kids can stand.  On three, the students go to line order, which is a very specific, students line up single file.  They’re meant to have out an independent reading book at all times during this time so that during transition while they’re waiting in line, while they’re walking, they’re reading.  Everything is done completely silent.  The students line up and then leave the classroom, file out single file to their next class.
Between 8:40 AM and 4:40 PM, teachers have a plan period and a half-hour for lunch, during which time most teachers remain on duty while trying to eat.  Because lining up and marching has to be performed perfectly or it must be done again, lunch usually lasts less than the thirty minutes allotted in the schedule.  Throughout the day, teachers carry clipboards and spend their time maintaining total compliance and teaching content. 
Following the minute details of management, demerit, and punishment plans takes up considerable chunks of class time.  One teacher described the “demerit clipboard” this way:
And I really struggled with this demerit clipboard, because there was a whole, you know, each type of different demerit had a different number from one to nine. And you had to note it in a certain way. And you had to put your initials. And you had to put it next to the student’s name. So finding all of this on a clipboard that’s legal size with 30 kids on it, while you're in the middle of a lesson on the spot, so you [must] remember to record it—it can interrupt your whole lesson and derail it. I mean, some teachers can just do it quickly. At [another charter school where she taught previously], they have a barcode scanner and they just literally scanned the student’s name and the kind of demerit. And they do it instantly. I joked that they should just put the barcodes on the kids’ foreheads, you know?
The after school KIPP teacher experience looks something like this: At 4:40 students begin to get ready for home, and at 4:45 KIPP teachers march their students to the buses.  The teachers board the buses with the students to make sure everyone is settled with something to read.  Teachers leave the buses and are instructed to wave until the buses are out of sight.  Teachers then walk back to the building, where they tutor children until 5:45, except on Mondays and Wednesdays, when professional development meetings last until 8PM. 
Afterwards, teachers compile data for exit tickets that provide “concrete data” that children have learned what was expected of them that day. Then teachers organize their classrooms, prepare lessons for the next day (which must be typed later and turned in to the school leader each Friday), and make sure the independent worksheets are ready for the next morning.  By 8 or 9 PM, it’s time to head home to fix dinner and grade papers.
“Trying to put your finger on every potential leak”
         One teacher, who compared his 80-100 hour weeks at a KIPP school to “sprinting a marathon for two years,” provided insight into KIPP’s control strategies and the “intensity” with which they are maintained.  At his school, silence was not only maintained at school—it was also enforced on school buses—by teachers.  There are no ellipses in this excerpt, as it is verbatim from the audio transcript:
It wasn’t just working on being at the office or something like that.  It was we had to create and own an environment that was difficult to manage, and had to do that over a very long period.  I’ll give you two or three examples that will hopefully illustrate what I’m speaking about.  In the mornings, we decided, or the school decided, that kids should be reading as much as possible.  The bus drivers picking the kids up and dropping them off wouldn’t be able to discipline them or create the same kind of culture that we had expected of our students and that a lot of the culture that we’d created would break down on the way to school and after school.  We thought that if kids were unsupervised on those buses, they would inevitably lead to some sort of drama, fighting or conflict of some sort, and that would carry into the school day and distract them from their learning. 
         Our kids came in performing well below grade level, and we were trying to get them not just on track and caught up, but prepared academically and propel them forward.  We felt that was a risk that we couldn’t really take in terms of the amount of potential disruption that might come from getting off the bus with fires to put out before 7:30 in the morning.  Our solution was to ride the bus with the students.  This is actually something that a bunch of schools do.  I don’t think we were the only ones to do it.  What we did on the bus, was we had policy that we introduced that kids were not allowed to talk.  They could read their books.  They could look out the window.  They could sleep.  They could just relax.  But you’re getting ready for school—get yourself prepared.  Take a moment, gather yourself, read.  That was the policy.  As you can imagine, that’s not something that’s very typical for a group of 10, 11, 12 and 13 year-olds to abide by, especially at 6:30 in the morning. 
Actually it was certainly harder on the way home from school. It became an exercise in discipline, where the teacher was expected to ride the bus each day, either going out or coming back or sometimes both.  The ride would be an hour and you had to sit there and make sure that the kids didn’t talk.  That’s an extra hour or two added on top of the school day that’s already extremely intense where as a teacher I felt like I had to be extremely focused.  I had to be extremely professional.  I had to be extremely consistent.
         In retrospect, the benefit of that policy was probably extremely limited.  But it was something we decided to do.  We were on board with it.  We executed to the best of our ability.  However, it had a long-term cost of creating this experience for the teacher that was very intense.  And the experience with the kids that were very intense, too.  That created, as I mentioned briefly earlier, almost a pressure-cooker kind of environment where you felt—or I should say our strategy was trying to put our fingers on every potential leak.  But you’d feel like it’s going to explode if you’re not putting your hands in the leak.
Upon reflection, this teacher characterized his school’s approach as the “far extreme of control,” which, for him was “something that creates teacher turnover every two years.  It creates kids dropping out of the school.  It creates nervousness and stress among parents and students.”   
Silence and Stress
         Enforced silence is one of the chief sources of stress among KIPP teachers.  Failure to meet silence expectations is attributed by school leaders as teachers’ shortcomings, rather than any examination of unrealistic expectations of children with real social needs that were not being met:
…there is a huge pressure in terms of behavior and the way that kids act in your class ….I feel like if there is ever any behavior issue, it is automatically the teacher’s fault, when I don’t necessarily feel like that is true and those expectations that the kids are supposed to be silent in the hallway, they need to be silent when they are in your room, they need to be SLANTing and tracking you and silent when you are talking—I just feel like it is not realistic, especially developmentally, to expect that from middle school kids.
So it is just frustrating because they are just doing what is natural to them to kind of interact and like play around, and I feel like there is a big pressure for the kids not to be acting that way.  Then if they are choosing to act that way, it is your [the teacher’s] fault, like “well what should you be doing, you need to be doing more for them to be not acting that way.”
         As this same teacher noted, the emphasis on silent behavior had so sensitized her to that expectation that more teaching energy was going into enforcement than into offering more effective learning strategies.  The result was a focus on constant policing rather than teaching:
In terms of teaching, retrospectively if I look at it, it has made me a worse teacher because now I feel like I am constantly thinking of these expectations that I am supposed to have in terms of behavior—having them be silent and all those things—and I feel like it is making me a bad teacher because it is like constantly looking at the negative, “oh they are not doing good.”  I feel like it has not helped me to focus on the positive things as much.
From Stress to Distress
I feel like there are a lot of really good teachers who did leave and it wasn’t because they were bad teachers; it was just because they couldn’t deal with the pressure and the hours and the stress that is kind of put upon people (1177).
         A former KIPP teacher was headed for a visit to the KIPP school where he had resigned the year before.  He had found a different job since leaving KIPP, and he was able to sleep again.  He had started to gain weight, and his hair was starting to grow back.  While at KIPP, he had made friends with other KIPP teachers, and he missed the kids that he had come to know, despite all the organizational rules that discouraged that from happening.  He recalls:
I was walking down the sidewalk outside of the school, and I ran into one of the teachers who was going to the deli on the corner to go grab a Gatorade, and he said, ‘every time I see somebody who’s left this school, they always come back looking ten times happier.’ So when I think about KIPP now, I think about how grateful I am that I’ve been able to get some sleep and have some of my hair start growing back.
Other teachers found school time consumed a great deal of personal time, which brought added stress from a loss of connection with family and friends.  One teacher who had her first child while employed at KIPP placed the baby with her mother during the four years she spent teaching there.   She supported her mother so that her mother could retire and be the full-time caregiver for the child.  She offered this tearful explanation:
At the end of the school year in ____ is when I left, and my son started school that same year.  That’s when I realized I just had missed out on one of the most important things in my life.  He was my first child.  I had him late.  Yeah, that was hard.  I realized I didn’t know my four-year old because I had spent four years focusing on KIPP.  I was a first time parent so I didn’t understand.  I actually I gave him to my mother.  My mother quit her job to raise him.  I supported my mother and myself so my mother could retire from her job to take care of my baby so I could work for KIPP.  Then I realized it wasn’t worth it.
         Teaching at KIPP has many stressors, but factors related to organizational policies and practices, time demands, and the weight given to tests figure significantly to most of the teachers interviewed.  The following quote provides insight into how all three of these factors figure into the stress that this teacher acknowledged:
. . . there’s a lot of stress to do good in terms of making the [state testing] goals, and also for each teacher wanting that group to do at least as well as the teacher the year before, and in a lot of cases because you get a lot of turnover, that’s a big thing to hold over somebody.  It’s like, ‘Well, this teacher got this [state] scores for her English class,’ so there’s a lot of stress on that, and that’s a lot for the English and math departments, which are tested more often.  There’s also stress that the other departments have to cram stuff into a smaller space in time, because a lot of time is spent on those two subjects, Math and English, because those are the ones that are tested, and so I know that I had to squeeze a lot of curriculum into a smaller space in time that I shouldn’t have had to do. 
         This same teacher said the stress was “more continuous” than in his previous school, where test prep had been restricted to the month prior to the state test.  As with the other KIPP teachers I talked with, this teacher’s school did not hire substitute teachers, for fear of altering the behavioral regimen.  Outsiders could not be trusted to maintain the level of academic and behavioral compliance that the KIPP Model demands, and as this teacher put it, “it was hard to get anyone trained to be a sub.” 
         Because other KIPP teachers would have to take up the slack when a teacher was ill or needed to be off for other business, the teachers I talked with only took off for emergencies. Even then, the principal might “call you at home, and say, ‘can you possibly come in for part of the day?’  And you could be really throwing up, or whatever, and it was like you feel so guilty, and you’re trying to get in.”
         One teacher, who compared KIPP with her other charter school experience, found KIPP less fulfilling and less successful, even though she made between three and four thousand dollars more per year at KIPP:
. . .if you break down how much I am making an hour, I am making way less an hour than a public school teacher is and I am just constantly stressed and worried about school.  This is something that I didn’t experience in student teaching or when I was at the other charter school in New York:  it was a lot more laid back than KIPP, and the day was not as long as a KIPP day, either.  And I feel like that school was way more successful than the KIPP that I am at now.
Another teacher, who had landed a public school teaching job in a wealthy community after leaving KIPP, had a similar sense of relief that came when the yelling stopped.  She apologized for using the military analogy, but she could not come up with a more appropriate one:
I felt like I was almost coming out of, I don’t feel totally right saying this, but I guess I can, in a minor way, understand how military might feel coming home. And again I don’t feel totally right saying that, but that is the only parallel or metaphor I can make right now, but just sort of like this kind of shell shock sort of feeling and then like coming to a place where you know people are normal and act like humans.
That is kind of how it felt, and I just felt so grateful. I was like wow, we have books, like wow, I don’t have to yell at anybody for talking, and I can actually sit down and have lunch. I didn’t feel so stressed, and I almost didn’t know what to do with that feeling. So it was just liberating, and I felt like I didn’t know what to do with all this liberation.
         After the grueling work schedule at KIPP Model schools, the time left for family is often consumed with trying to recover from mental and physical exhaustion.  One teacher, ironically, pointed to the weekends as her low points while teaching at KIPP:
. . . I was just absolutely exhausted, my body was exhausted.  That happened so often, I would just sleep like all day on Sunday.  Where most people look forward to the weekend to run their errands, to spend time with their family, that would be my time to sleep.  Everyone knew it, like don’t call her—she’s sleeping.  Those are my low points—the weekends. 
        It is not uncommon for KIPP teachers to have no more than 4-5 hours of sleep each night during workweeks of 12-14 hour days. One KIPP teacher spoke of a principal whose sleep deprivation while at KIPP had contributed to an inability to get pregnant.  Upon resigning from KIPP, “she slept for about a month straight and she was pregnant a month later.  So it had a pretty profound impact on other people.”
        One teacher, who was still under the care of a therapist following her time at KIPP, said that she would leave her house at 4:30 AM in order to get to school at 5 AM.   This way she could get her photocopying done before most of the other teachers showed up, and get to her classroom by 6 AM so that she should organize for her day, which started at 7:05 with students.  Having gotten to bed around midnight after getting home between 9 and 10 PM, the alarm came earlier each day that she followed this routine.  She said of the hours she kept, 
I was never the last person in the parking lot, and I was never the first person in the parking lot either. And I worked non-stop. I also have to say KIPP was very isolating when you work those horrible hours.  I left my family and my boyfriend in Arizona and I wanted to forge this great life out here. And I wanted to be this great teacher. And I gave it everything I had.
By October, she was getting up at 4:17 AM and out the door by 4:30.  It was at this time that she said, “I stopped taking regular showers because I wanted to get 20 more minutes sleep.” 
While many KIPP educators lose sleep because of the workload and the pace of work life, one teacher found KIPP colleagues who were “very, very, very much committed,” but he said that, because of the workload, he was unsure “how many of them last a long time at KIPP” (1170).  Sleep is not only an issue for teachers, but also for students.  One former teacher told me that the first thing he would change about KIPP would be early start time, which is commonly around 7 AM. 
He framed his argument in terms of test scores achievement:  “How much are we harming these kids’ scores and their cognitive development by depriving them of sleep because a lot of them are still going to bed at midnight or 1 o’clock in the morning and then getting up at 5 or 6 in the morning? Many of them have the same schedule as the KIPP teachers.”
From Distress to Other Health Issues
You wind up sacrificing your physical health and most of your social life. . . . I put my relationship with the students before my physical health.  (1166).
         With the kind of pressure-filled hours described above, it would be surprising if teachers reported no ill health effects.  All the teachers interviewed did report negative health effects from working inside No Excuses pressure cookers.  The mental health effects range from PTSD symptoms, anxiety disorders, unusual sadness, nightmares, depression, anger issues, nervous exhaustion, emotional and mental breakdowns, and classic teacher burnout.  One teacher spent a week in the hospital in November after a “mental breakdown,” and another who was still in treatment described her condition as a “nervous breakdown.”  A third teacher reported that she saw four teachers have “complete nervous breakdowns:”
I can definitely in my mind right now identify four teachers that I saw unravel that had a nervous breakdown and I would just explain it as crying and shaking and talking and not making sense.  Babbling, a lot of babbling.  Asking for help.  Crying.  I felt horrible. 
         Self-reported physical manifestations included weight loss, weight gain, bad nutrition, more colds and respiratory infections, poor hygiene, and alopecia.  One teacher was so unavailable to his partner as life became “completely the job” that her partner began an active job search for her while she was trying to survive the rigors of KIPP.  She said, “I lost a lot of weight.  A lot of people voiced concern about my weight.”  The job search proved productive, and she left KIPP for health, relationship, and ethical reasons having to do with the treatment of special education students.
         Another teacher who suffered from alopecia and other work-related illnesses resigned from KIPP for health reasons.  She talked about not being able to “handle working there,” even if it meant not finding another teaching job immediately:
I am just going to hope that I can find something. I just decided that regardless of whether or not I get another job—I would like to have another job—I just can’t handle working there. It is just not worth my health to do that for another year, because even other than my hair falling out, I have had a lot of other health issues.  I have had pneumonia, I have had my hair fall out, I have had stomach problems, and I have had a lot of anxiety and it is just not worth it at all. And I am constantly in a bad, bad mood and my boyfriend tells me like all the time that he has noticed a big change in the way I am since I have been working there.        
         One teacher I talked with came to KIPP after teaching for some time in her home state and winning praise as Teacher of the Year in one of the schools where she worked.  She had a non-education Bachelors degree and a prior successful career in her field.  Her Masters was in Education, and it was a short video that she saw during her graduate school experience that sparked her desire to be a KIPP teacher:  I really bought into the mission about helping kids who might not otherwise go to college. And also the video made me think, like, if they didn’t go to KIPP, they were going to die. Like it was very drastic.  And so I worked really hard. And in the back of my mind, I really just wanted to be a KIPP teacher.”
She finally made the decision to apply out of state to a KIPP school, and she was hired to teach middle school.  She packed up her car with all her teaching materials, which left room for a single suitcase, and moved to begin her dream job at KIPP.  The new KIPP regimen proved brutal.  In order to get everything done the KIPP way, she found it necessary to work 16-18 hour days, which left no time for anything else, including taking care of herself:
…I had no time to grocery shop ever. So my roommate would do all the grocery shopping. She would cook things for me, and she would leave them in the fridge. And if it wasn’t for her kindness, I think I would have starved to death. And I’m not exaggerating. I’m so embarrassed that I let my life get like that.
         When she told her grade level supervisor that she was breaking down and thought she needed to see a therapist, she was sent on to the principal, whose first reaction was to ask to observe her class.  Following the observation, the principal opened their meeting by saying, “I’m just really worried about you. I hear you have no joy.”  It was at this point that she “fell apart” and told the school leader that she didn’t know how much longer she could “take this.”
         A distinct lack of joy is not only apparent among many KIPP teachers.  One teacher noted that students “look like adults walking down the hallways.  They’re stressed.  One thing you always notice at the [KIPP] school I was in—you will not see a student smiling.  You would not see a teacher smiling.  I mean the difference in the school culture from [my] previous school is so different—like the kids were literally breaking down.”

Monday, August 22, 2016

John Oliver's Charter School Primer


High school grades vs. the SAT (grades win)


Sent to the Boston Globe, August 22, 2016

"Colleges cutting ties with the SAT" (August 22) is supported by research. In a study published in 2007, UC Berkeley scholars Saul Geiser and Maria Veronica Saltelices found that adding SAT scores to high school students' grades in college prep courses did not provide much more information than grades alone. In 2009, William Bowen, former President of Princeton University, Matthew Chingos, Senior Fellow of the Urban Institute, and Michael McPherson, President of the Spencer Foundation, reached similar conclusions in their book Crossing the Finish Line: Completing College at America's Universities.
In other words, it appears that teacher evaluation of students does a better job of evaluating students than standardized testing does: The repeated judgments of professionals who are with students every day is more valid that a test created by distant strangers.

Stephen Krashen
Professor Emeritus
University of Southern California


US Department of Education Executive Director in Spat or Brutal Brawl?


As usual, select the the news source you choose to believe on education coverage:

 

 A) US Department of Education

William Mendoza, Executive Director

 

B) Politico

Obama official faces questions about Redskins jersey altercation


C) News 9  

Oklahoma Native American Says He Was Attacked Over Redskins Shirt

 

D)  Durango Herald

Punches fly over Redskins jersey

 

E) Washington Times

Autistic Native American says White House official attacked him over Redskins jersey

 

F)  Daily Mail

with lots of pictures

Obama policy adviser 'called autistic Native American man a "weetard" for wearing a Redskins sweater, spat in his face and then beat him so badly he needed THREE surgeries'


G) None of the Above


NOTE: William Maxwell has been mentioned once in the  New York Times, Nov. 14, 2014
 

Friday, August 19, 2016

Education Technology, Surveillance and America's Authoritarian Democracy

"The NSA has nothing on the monitoring tools that education technologists have developed to 'personalize' and 'adapt' learning for students in public school districts across the United States"  
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 audio and video recording technology is increasingly being used 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.

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