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Below are the notes and slides from my talk yesterday at Columbia University. The talk was part of the university's Conversations About Online Learning series, and my trip was sponsored by the Office of the Provost and the Columbia Center for New Media Teaching and Learning. A big thanks in particular to Alex Gil for facilitating my invitation to speak there.
I am incredibly honored to be here to speak to you today. Columbia University has a very special place in my heart. I never attended, but my Uncle Jim did. He graduated in 1960 and still lives in the same little rent-controlled apartment just a few blocks from here. He became an entertainment journalist after he graduated, writing for LIFE and for People magazine.

I grew up in Wyoming, and my Uncle Jim would only return once a year -- for like 48 hours -- for Christmas. He would return with the best stories and, often, with the best movie swag.

To me, he represented escape. Escape from Wyoming. Escape to New York. He represented all things Big Apple -- he knew movie stars, he was a Democrat. None of those can be found much in Wyoming -- except maybe in Jackson Hole.

And he was a writer; I wanted to be like my Uncle Jim. I wanted to reject the conservative constraints of "home." I wanted to be a writer, and I was both thrilled and panic-stricken when an aptitude test I took in junior high came back with only one job recommendation for me: freelance writer.

Finally, after years of dropping in and out of school, out of college, and out of a PhD program and disappointing my Uncle Jim plenty along the way, that's what I do: I write.

I was always intrigued by my uncle's profession and his politics, which I somehow credited in part to this university. I was particularly fascinated, although the Vietnam War most affected my dad's generation and although my Uncle Jim attended Columbia almost a decade earlier, by the anti-Vietnam War efforts of students here, by the takeover of parts of the campus.

Much of my work as an academic -- I'm a recovering academic now, for what that's worth -- dealt with student protest. But before I started my scholarship on student activism, I'll confess to you this: as a teenager, I had a weird sort of crush on Columbia student and SDS leader Mark Rudd.

In 2003, shortly after the documentary about the Weather Underground was released, I had the chance to hear Mark Rudd speak. He's a math instructor now at a community college in New Mexico. But he came to the University of Oregon where I was a graduate student -- working on a dissertation on the European avant-garde and student protest theatre, believe it or not. And Eugene, Oregon at the time, much like Columbia University in the late 1960s, was a "hotbed" of radicalism.

After the screening of the Weather Underground film, someone asked Rudd what he thought about the government's surveillance capabilities today. ("Today" being a decade ago now, I should make really clear.) Rudd and the other members of the Weathermen were surveilled as part of the FBI's COINTELPRO project -- a series of covert and often illegal efforts to investigate and discredit the left in the 1960s.

And although not explicitly stated, the question from the audience was certainly related to the FBI's investigation and infiltration of radical environmental groups in the Eugene area in the late 1990s and early 2000s.

But Rudd scoffed at the question. Even with the enhanced capabilities of machines, he insisted, human investigators just aren't that good at sifting through the data. And it was likely, he argued, that computer-based surveillance would continue to be more about the politics of "police work."

Fast forward from that Q&A session to the summer of 2013, dominated by the news leaked by Edward Snowden of the NSA surveillance program, of massive data harvesting and data warehousing by the federal government. Cell phone records. Email. Social networks. Metadata.

This is, I realize, a very long-winded and circuitous introduction to what I want to talk about here today: data-mining and education.

But I want to set the stage with these interrelated stories. I want you to keep in mind, as I talk specifically about learning analytics, big data, and education algorithms, more generally our histories of overreaching government surveillance, our histories of student radicalism, our personal histories of likes and dislikes and dreams, and our histories -- personal and institutional -- of standardized testing and aptitude testing. How do these shape who students become? Will data analytics change this?

Of course, the emerging fields of education data mining and learning analytics rarely if ever frame themselves in terms of surveillance and policing. Not explicitly at least.

The promise of learning analytics, so we're told by education researchers and -- with much more certainty and marketing finesse -- by education companies, is that all this data that students create, that software can track, and that engineers and educators and administrators can analyze will bring about a more "personalized," a more responsive, a more efficient school system. Better outcomes (which we can translate cynically as: higher test scores, higher class and college completion rates).

The claims about big data and education are incredibly bold, and as of yet, mostly unproven.

Take Knewton, for example, one of the corporate leaders in the sector. Once in the highly lucrative business of test prep, Knewton has rebranded to offer "adaptive learning" -- partnering with publishers to create content delivery and assessment software that moves students through educational materials "at their own pace."

That's the PR spin, at least: with big data, Knewton engineers can now precisely identify a student's strengths and weaknesses and "learning styles" (ignoring, I should add here, the evidence that learning styles do not exist) and guide students through the next-best content nugget so as to learn with maximum efficiency. As a recent story about the company put it, "If you learn concept No. 513 best in the morning between 8:20 and 9:35 with 80 percent text and 20 percent rich media and no more than 32 minutes at a time, well, then the odds are you're going to learn every one of 12 highly correlated concepts best that same way."

Knewton boasts that over a million data points are created by students on its platform every day, all of which feed its algorithms and its recommendation engine, all in turn in the service of delivering lessons that it claims are perfectly and personally adapted to each and every student.

A million data points a day. A slightly different stat that executives from the company also bandy about: "a million data points for each student who's taking a Knewton platform course for a semester." Regardless. That is a lot of data about students.

It's still just a tiny drop in the quintillions of bytes of data are created every day. And the million data points a day generated on the Knewton platform are just a tiny drop of the data that students are creating -- in "formal" and "informal" settings, via "sanctioned" and "required" and DIY ed-tech tools, intentionally and unintentionally.

So what data are students creating? What data are schools and software companies gathering?

Traditionally, we've thought about "student data" in terms of what's on the transcript - that is, demographics, major, and final course grades. Student data includes test scores. Individual assignments. Attendance. Add to that perhaps, behavior and disciplinary records. "This will go down on your permanent record!" as many of us have been informed.

But schools and their administrative and instructional technologies track so much more these days: library check-outs. Gym visits. Inter-mural sports participation. Cafeteria and bookstore purchases. Minutes from student meetings. Times in and out of the dormitory.

Much of this data exists in software silos that are disconnected. But more and more, companies are starting to push for the aggregation of student data into analytics tools that can be sold in turn back to the school. Learning management system log-ins and duration of their LMS sessions. Blog and forum comment history. Internet usage while on campus. Emails sent and received on via university email accounts. The pages students read in digital textbooks. The passages they highlight.

There's more too, that gray area of students' computer usage, via software that isn't necessarily administered by the school: students' search engine history. Their social media profiles. Time spent on Facebook while in class. Videos watched on Coursera or Khan Academy or Udacity, along with if and where they paused the videos. Exercises completed on any of these platforms. Their Wikipedia visits. Their downloads. Their uploads. Their levels on Grand Theft Auto V.

All their keystrokes and mouse clicks, logged.

Those last items are, along with biometric data, how Coursera says it plans to confirm students' identities for its "signature track" MOOCs, that is those courses from which students can pay for an official certificate. These are also the data that Coursera says will give it incredible insights into course design.

"By collecting every click, homework submission, quiz and forum note from tens of thousands of students," TED describes Coursera founder Daphne Koller's talk, MOOCs have become "a data mine that offers a new way to study learning."

Actually Coursera has some 4.3 million users, not simply "tens of thousands." So when we think of MOOCs -- and education technology more broadly -- as "a data mine," it is indeed a massive one.

Now although I write about education and technology for a living, my formal academic training is in neither area. I'm a literature and language person, and so when I hear "data mine," my first thoughts aren't about statistics and algorithms and mathematical models. I think about metaphor. I think about cultural history.

The phrase "data-mining" is quite new -- less than 25 or so years old -- although yes certainly, statistics and algorithms and pattern recognition and the analyses based upon these have a much, much longer history. In the 1960s, statisticians referred to the pouring through of data without an a-priori hypothesis as "data-dredging," a practice that carried a negative connotation, as "dredging" would, I guess.

It's worth noting too that the concerns about data and its misuse -- particularly with regards to violations of privacy -- extended to the public-at-large at that time. Indeed, as banking, healthcare, and government services were becoming increasingly computerized in post-war America, the 1960s and 1970s saw the passing of several laws -- many still on the books -- addressing the collection, storage, and sale of people's data, including FERPA, the Family Educational Rights and Privacy Act, the law that governs the privacy of students' education record.

Data-dredging. Data-mining. Technology processes, sure. But also really interesting metaphors.

Dredging data conjures up the image of searching or excavating a large, fluid pool of information. Dredging up information from the bottom of the pool, information that's been buried, that's otherwise inaccessible. And, to continue the metaphor, despite the importance or value in what's being harvested, dredging in the physical world is largely recognized to disturb the ecosystem and to leave behind toxic chemicals.

Mining data might suggest a more targeted resource extraction. It certainly suggests a more lucrative one. And ideally, I'd argue, concerns about damage to the ecosystem persist. And if we're reluctant to talk about the environmental impact of minerals mining, we're ignoring that impact altogether in data-mining.

"Data is the new oil," headlines proclaim. "Data is just like crude," says the market analyst. "It's valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value."

"Data is the new oil," says the investor, urging startups to locate and mine resources currently untapped.

"Data is the new oil," says The World Economic Forum. "In practical terms, a person's data could be equivalent to their 'money'".

"Data is the new oil," say tech bloggers and journalists alike.

Among the earliest references I can find of this phrase, for what it's worth, this in 1999: "Information is the new oil. Drilling new sources of information."

Now again, let me remind you. I'm from Wyoming -- the land of Dick Cheney. 48% of my home state is public land, but the minerals rights are leased or sold to private companies for mining (coal, uranium, natural gas, and yes oil). (The tension between public and private when it comes to education data is particularly drought.)

Wyoming is also the site of the Teapot Dome scandal, which before Watergate, was the biggest scandal in the history of the government. In case you easterners didn't pay attention during that unit of history class, Teapot Dome was when the Harding administration gave private companies opportunities to mine public lands in Wyoming (and in California) without going through the proper bidding process.

When I hear "data is the new oil," I think about these histories, the sorts of relationships that have long been forged between government and corporate entities.

I get it: to call data "the new oil" is particularly resonant in our energy-hungry and fossil-fuel reliant economy. And for what it's worth, some data scientists have pushed back on the "oil" metaphor -- or at least some of the uncritical glee surrounding its usage to simply talk about the potential for profits. Jer Thorp, an educator and the former data artist in residence at The New York Times argued that the "data is the new oil" metaphor is deeply flawed. Data isn't something that lies beneath the surface, just waiting to be extracted. Thorp writes -- and apologies for quoting him at length here -- that,

"Perhaps the "data as oil" idea can foster some much-needed criticality. Our experience with oil has been fraught; fortunes made have been balanced with dwindling resources, bloody mercenary conflicts, and a terrifying climate crisis. If we are indeed making the first steps into economic terrain that will be as transformative (and possibly as risky) as that of the petroleum industry, foresight will be key. We have already seen "data spills" happen (when large amounts of personal data are inadvertently leaked). Will it be much longer until we see dangerous data drilling practices? Or until we start to see long term effects from "data pollution"?
One of the places where we'll have to tread most carefully - another place where our data/oil model can be useful - is in the realm of personal data. A great deal of the profit that is being made right now in the data world is being made through the use of human-generated information. Our browsing habits, our conversations with friends, our movements and location - all of these things are being monetized. This is deeply human data, though very often it is not treated as such. Here, perhaps we can invoke a comparison to fossil fuel in a useful way: where oil is composed of the compressed bodies of long-dead micro-organisms, this personal data is made from the compressed fragments of our personal lives. It is a dense condensate of our human experience."
If we are to embrace the "the new oil" metaphor, Thorp insists that we do so critically, thinking through all the implications, and not merely those implications those that have the "mining" executives rubbing their hands together in glee, anticipating the profits to be made.

The metaphor remains the dominant way we talk about data. Add to "data as oil" or "data-mining." there's the phrase "data exhaust" too, one that I've heard used with increasing frequency. Again, these terms really do matter. "Data exhaust" -- as though even the scraps or waste from what we do online, with our various computing devices, with our phones is valuable -- data exhaust is metadata.

There's a sense with that phrase, "data exhaust," that we're able to make some sort of ecological use of materials that would otherwise simply be waste. Like recycling materials, something that would be tossed aside by its creator or user becomes valuable to someone else. The exhaust is open to being "dredged," sucked up and filtered through by companies and, now we know, by our federal government.

The promise of big data is that mining will uncover something of great value, something that we haven't been able to unearth until now -- thanks to the massive quantity of data, thanks to the massive computer processing power: the cure for cancer, perhaps, or for any number of medical illnesses; in our case here, a cognitive roadmap for how each of us learn. That's certainly something we hear from those in education technology -- that big data and education analytics can crack open the "black box" of learning.

The value uncovered by data mining can be, in other words, a scientific breakthrough -- "an answer." And/or, the value can be money. Indeed, it's helpful to think about the uses of big data and analytics we've already seen: high-speed trading, Internet advertising, e-commerce recommendations.

These, I think, might provide hints of why we see much of the excitement and investment in technology companies right now: data.

The recent explosion of technology startups -- whether mobile tech, social networking sites, or ed-tech -- reflects this. Many of these new companies charge nothing for their product, yet they're raising millions of dollars in venture capital. The business model might develop eventually, sure. These companies might go on to be "the next Google." Even if revenue never materializes, there might be some interesting technology under the hood that make them a target for acquisition. But if nothing else, there'll be data -- lots and lots of user-generated data -- that someone believes can eventually be monetized. Think Twitter. Think LinkedIn. Think Facebook.

Think too of the many, many education technology startups that are free to use:

Remind101, a company that lets teachers and students communicate "safely" via text-message, has raised $3.5 million

Codecademy, a learn-to-code website, has raised $12.5 million.

Udacity, one of the MOOC startups, has raised $20 million.

Edmodo, a social network for schools, has raised $40 million.

Coursera, another one of the MOOC startups, has raised $65 million.

Despite all these free tools, there is a huge market for ed-tech products. Schools, students, parents do and will pay. And no doubt, technology does have incredible capacity to improve and scale our access to education.

But that should be what we cultivate ("to cultivate" -- a different verb, different metaphorical language, no doubt, than "to mine").

Tech should not be used simply, as London Knowledge Lab professor Diana Laurillard has said, "to access masses of data from desperate would-be students." She was referring to the question of scale and access and technology and MOOCs and specifically responding to a round of funding that Coursera had just raised. "Venture capitalists," she said, "are not spending $22 million to nurture students."

Indeed. It seems likely they are spending it because "data is the new oil."

And if educational data is indeed "the new oil," how do we make sure that education technology isn't poised simply to extract value from students? How can it deliver value -- cultivate value, cultivate minds?

We must ask, for starters, "who owns students' data?" Because even though laws like FERPA purport to protect the privacy of students' education record, there isn't a clear provision that states the student record belongs to the student. And as I noted earlier, that student record has expanded to include much much more "data" than when the law passed in the early 1970s. Furthermore, recent revisions to FERPA make it easier for schools, at best the guardian of that record, to release student data to companies that provide educational services and software. The learning management system, for example, the adaptive learning software, the digital textbook publisher, the online course provider.

Of course, this is complicated by the fact that FERPA's protections -- as out-of-date and as frustrating as they have become in a digital world -- only cover students in formal academic settings. These protections only cover students enrolled in programs which receive federal funds under certain Department of Education programs. That leaves open a whole swath of companies -- many new for-profits in the education technology sector -- that need make no pretense of protecting student data under this regulation.

Here is part of the Terms of Service from the World Education University, which, incidentally, offers online courses for free:

"WEU may share your information with any third party outside of our organization as necessary to underwrite free educational offerings. WEU may contact you about specials, new products or services, or changes to this privacy policy."

We can make a joke that "no one reads the Terms of Service." But frankly, I'd wager that no students -- or very, very few -- think through the ways in which their personal data will be used by any products and services associated with their schooling -- whether they are in an ad-supported educational program like WEU or whether they're in a VC-supported educational program like Coursera or whether they're in an endowment-rich school like Columbia or whether they're paying their own way through their local community college.

Technology writer Douglas Rushkoff has long argued, "if you're not paying for the product, you are the product." But I think it's actually far more complicated than that. When it comes to our metadata, it seems we're becoming the product either way. And when it comes to schooling, we're already well accustomed to talking about students as "the product" of the system: heads to fill with information; lives to shape; and now metadata to mine.

So what will be the role of the algorithm in education? How will big data and learning analytics shape the decisions we make -- in the classroom, in institutions; as students, as professors, as administrators, as parents?

Degree Compass was a startup founded at the University of Austin Peay in Tennessee. The point was to take students' performance in certain classes, along with enrollment data and the patterns of "similar students," to point students em to subsequent classes that "the algorithm" said they'd do well in.

From the Austin Peay website:

This system, in contrast to systems that recommend movies or books, does not depend on which classes are liked more than others. Instead it uses predictive analytics techniques based on grade and enrollment data to rank courses according to factors that measure how well each course might help the student progress through their program. From the courses that apply directly to the student's program of study, the system selects those courses that fit best with the sequence of courses in their degree and are the most central to the university curriculum as whole. That ranking is then overlaid with a model that predicts which courses the student will achieve their best grades. In this way the system most strongly recommends a course which is necessary for a student to graduate, core to the university curriculum and their major, and in which the student is expected to succeed academically.

Degree Compass was acquired by the learning management system Desire2Learn in early 2013 for an undisclosed amount of money.

It's certainly a more sophisticated way of suggesting courses, which is -- in some ways, about suggesting career opportunities -- than the aptitude test that I took back in the 1980s, the one that told me the only job it recommended was "freelance writer." That test asked me questions like: "Do you like lifting heavy objects?" Um. No. "Do you like working outside?" Not really. No. "Do you like to read?" Yes! "Do you like to write?" Yes! "Do you like following orders?" Hell no. "Do you like being in charge of people?" I'm 14. What do I know?! But that test, the Strong Interest Inventory, was based on data. It was based on a questionnaires and research, and it's still touted for its predictive capabilities, even though its easiest version was created in the late 1920s.

And so: imagine, some might say, what we could do with more data. Imagine what we could do with better data. Imagine the predictions we could make.

We could identify which students are likely to be the most successful academically. (In a future where graduation rates are tied to financial aid availability, imagine how this might shape schools' enrollment policies.)

We could identify which students are likely to be dropouts. (Of course, then we have to ask: what do we do then? When do we intervene? How do we intervene? To what end?)

We could investigate how students' performance in Algebra I is tied to their credit score later in life. We could investigate how their performance in The History of Western Civilization is tied to their voting patterns.

We could identify the lessons and the lectures and the assessments sets that "don't work." (You hear this from the folks at Coursera a lot who argue that MOOCs allow them to "fail fast" in this respect. Or, as my friend Mike Caulfield argues in response, you could actually hire good instructional designers and produce quality work from the start.)

We could identify which students are likely to make great biochemistry majors. (You have to wonder here, if the folks who write these algorithms would even suggest people become art history or creative writing majors.)

We could identify which students are likely to be successful entrepreneurs. We could identify which students are likely to become wealthy alumni and reliable donors back to the school.

And to bring things full circle, we could identify which students are likely to become radicals and dissidents. We could share that data with administrators and/or with authorities.

Data, we're told, will allow us to address our most pressing questions in education. But as the uses I've just detailed suggest, it matters who asks those questions, what constitutes those questions. These questions are what shape the algorithms that we build to answer them. And I'll add too that the metaphors we use shape the models we build as well. What does it mean if we decide student data "the new oil"? What does it mean if we view students (and their data) as a resource to be mined and extracted? What's gained? What's lost? What's depleted? Who profits? Who benefits?

I'm just not confident that it'll be those who are having their metadata mined.

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Student Data is the New Oil: MOOCs, Metaphor, and Money

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Below are the notes and slides from my talk yesterday at Columbia University. The talk was part of the university's Conversations About Online Learning series, and my trip was sponsored by the Office of the Provost and the Columbia Center for New Media Teaching and Learning.

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Avhandling om literacy og faktisk bruk av nettet i skolen

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Dan Friedman: The MOOC Revolution that wasn´t. 

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Dan Friedman: The MOOC Revolution that wasn´t. 

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Bak hypen om MOOC

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Editor's note: Dan Friedman is the co-founder of Thinkful.
Three years ago this week, Sebastian Thrun recorded his Stanford class on Artificial Intelligence, released it online to a staggering 180,000 students, and started a " revolution in higher education." Soon after, Coursera, Udacity and others promised free access to valuable content, supposedly delivering a disruptive solution that would solve massive student debt and a struggling economy. Since then, over 8 million students have enrolled in their courses.

This year, that revolution fizzled. Only half of those who signed up watched even one lecture, and only 4 percent stayed long enough to complete a course. Further, the audience for MOOCs already had college degrees so the promise of disrupting higher education failed to materialize. The MOOC providers argue that completion of free courses is the wrong measure of success, but even a controlled experiment run by San Jose State with paying students found the courses less effective than their old-school counterparts.

This shouldn't have come as a surprise. Online learning long ago solved the access problem: Between the 8 million people who have signed up for MOOCs, and the more than 1 billion downloads from Apple's iTunesU (Apple is quietly a larger force in online education than any upstart), we already know people want to take online courses. What we don't know is whether they can be as effective, or more effective, than sitting in a classroom. It's time to focus on that harder problem: engagement.

As an online learner myself it's hard to stay engaged: when I get home after a full day at work, the noble goal of learning new skills often is put aside with Netflix only a click away. Online learning needs to pass that test: it needs to be not only good for you, but enjoyable in its own right. Startups, big companies, and universities are finally focused on the true promise of online education: driving substantial learning at an affordable price. There are three areas that have shown promise towards that goal: mentorship, retention marketing, and new forms of learn-by-doing.

One-on-one mentorship was long ago found to be dramatically more effective than group instruction. Having the full attention of an instructor accelerates an individual's learning by focusing them on the right problems at the right times, and having a real relationship with one person provides students with accountability. At Thinkful, we see a spike in learning the day before students have sessions with their mentors. Students want to achieve more because of their relationship, and that motivation translates to more efficient learning. We're now working to apply that same social pressure throughout the week to bring up overall learning time further. Sometimes you just need someone you respect telling you to eat your vegetables.

Second, retention marketing needs to be brought to learning. There's an entire discipline in marketing to existing customers through email, phone calls and push notifications to keep them engaged with your product.

For example, if a Dropbox user views a page to learn about Dropbox for Teams, Dropbox sends an email saying, "Thanks for checking out Dropbox for Teams! Here's a few features you might not have known about..." By putting a product in front of the right users at the right time, online learning companies can instill good learning habits. Sometimes all that's needed is a simple nudge to remind a student to spend more time learning.

Third, new technologies enable methods of "learn by doing" that just weren't possible before we could deliver immersive experiences to people's laptops and phones. In the 1960's, Jerome Bruner expanded an educational theory known as constructivism with the idea that students should learn through inquiry under the guidance of a teacher to grasp complex ideas intuitively. That process of trial, failure, and then being shown the correct path has been proven to drive student motivation and retention of learning.

What we don't yet know is if that process of trial and failure can become 10x more engaging when delivered through a new medium such as Minecraft or Oculus. There's been promising early results around using Minecraft for learning, and we're excited enough about the possibility of using Oculus that we've got one shipping to the Thinkful office as we speak. These new immersive worlds promise to hold the attention of students in ways textbooks never could.

Finally, there's one other trick education startups use that works but hurts everyone in the long-run: making courses easier. Companies see low completion rates and cut important material as a "quick fix" to higher graduation rates.

When Udacity launched, its initial courses dove as deep into subjects as their Stanford counterparts. Now, many Udacity courses take as few as 40 hours to complete; if Stanford were giving course credit for a week of work, it wouldn't have the reputation. Soon we'll be hearing about the "dramatic rise" in MOOC completion rates, without a mention of how much easier it is to complete a course.

Those "improvements" hurt the entire industry as the actual outcomes - jobs, promotions, improved productivity - still won't be delivered. As we're seeing with the decline of the for-profit universities, quick fixes that sound good in the press don't work in the long-term.

The future of online learning isn't about accessibility: it's about taking what we already know works offline and combining it with what you can only do online to create the most engaging experience. We're all still searching for the right formula, but the ingredients will be the same as they've always been: Learning through exploration, thoughtfully designed for the right behaviors, with great teachers providing support.

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Editor's note: Dan Friedman is the co-founder of Thinkful. Three years ago this week, Sebastian Thrun recorded his Stanford class on Artificial Intelligence, released it online to a staggering 180,000 students, and started a " revolution in higher education."

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Kirschner et al:  Do learners really know best?

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Kirschner et al:  Do learners really know best?

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Om tre ´urban legends´i utdanning.

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Om software, big data som redcap i educational governance

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Neil Selwyn: Towards the critical study of data and education 

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Neil Selwyn: Towards the critical study of data and education 

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Tre myter om teknologi og utdaning

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De-mystifying MOOCs

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A second problem is that when MOOCs replace traditional courses, an extremely high number of students fail. A well-publicized experiment backed by Gov. Jerry Brown of California at San Jose State University flopped. In one of the MOOCs, just 25 percent of students passed; in another, only 50 percent passed, much lower rates than for the on-campus equivalents.

Those MOOCs were provided by Udacity, whose co-founder, Sebastian Thrun, is often credited with popularizing the massive courses after he opened his artificial intelligence class at Stanford to the world in 2011 and 160,000 students from 190 countries signed up. But what he ended up discovering was that he had designed MOOCs for the type of self-motivated, bright students he taught at Stanford, or the college student he was at 18, when he essentially taught himself using books at the University of Bonn's library.

"The basic MOOC is a great thing for the top 5 percent of the student body, but not a great thing for the bottom 95 percent," Dr. Thrun told me after he decided to shift the focus of his company last fall to concentrate on corporate and vocational training, and to charge a fee for courses.

Nearly all MOOCs originate from the world's top universities. Their instructors are accustomed to teaching the brightest students, and may not understand the motivations, academic difficulties and self-discipline of the average student.

What's more, for many instructors, the courses are on-the-job training in online education. Two-thirds of MOOC professors surveyed by The Chronicle of Higher Education in 2013 said they had never taught a fully online course before their first open online class. Many universities rushed their courses online in response to the initial hype, often choosing popular professors or volunteers rather than looking for the best teachers for the MOOC environment. A student who already has an advanced degree is far better equipped to navigate such a course, especially one covering difficult material.

The best online professor in a subject may teach at the local college down the street. "Could we be passing over a good instructor at a small college? Sure," says Daphne Koller, one of the founders of Coursera. "We have limited bandwidth to partner with individual professors."

Coursera and edX, the two main MOOC providers, are essentially acting as gatekeepers for American higher education online, replicating in their virtual world the pecking order in the physical world as determined by U.S. News & World Report rankings. Top colleges have earned their place in the rankings mostly because they admit the smartest students, spend more money on academics than anyone else and bring in the most research dollars. None of those attributes matter to the students the two providers want to attract but aren't reaching.

Many educators take the view that, using the metrics by which we judge traditional higher education (prestige, completion rates), MOOCs have failed to fulfill their original promise.

But those metrics don't take into account how MOOCs are being used right now. Students can register, with no financial risk, for as many courses as they want. Some might want to sample a particular lecture, or prepare a business plan for investors, or take a lesson for a presentation the next day.

Call it "just-in-time education." These students hadn't planned to complete the course, and they have nothing to lose when they stop taking it. The MOOC provides learning in chunks, at a student's own pace.

Take Leo Cochrane, who already has a bachelor's degree but took a free online class from the University of Virginia 's Darden School of Business to help expand his start-up air-purifying business. The course was perfect for the time-pressed entrepreneur. He had little inclination or money to follow a path that would take him to a traditional campus or even to an old-fashioned online course, with its rigid deadlines for lectures and completing assignments.

With a MOOC, he could watch video lectures on his iPhone while running on a treadmill and pick and choose what he needed to learn from the syllabus. MOOCs put students in control. Students can do as much or as little as they want at any time, one reason that many never complete the courses. Roughly one in 10 finishes.

The companies that rode to fame on the MOOC wave had visions (and still do) of offering unfettered elite education to the masses and driving down college tuition. But the sweet spot for MOOCs is far less inspirational and compelling. The courses have become an important supplement to classroom learning and a tool for professional development.

They are instruments in what George Siemens, who co-taught the first MOOC, in Canada in 2008, calls "the shadow learning economy," which happens alongside formal education, much in the way textbooks supplement courses.

That is the success story for massive open online courses as they graduate from the hype cycle's "trough of disillusionment" into the "slope of enlightenment," on their way to the "plateau of productivity."

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A second problem is that when MOOCs replace traditional courses, an extremely high number of students fail. A well-publicized experiment backed by Gov. Jerry Brown of California at San Jose State University flopped. In one of the MOOCs, just 25 percent of students passed; in another, only 50 percent passed, much lower rates than for the on-campus equivalents.

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Nærblikk på det digitale klasserommet

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19 essay om  hvordan internet har forandrer oss

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19 essay om  hvordan internet har forandrer oss

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Om hvordan personlige data blir salgsobjekter.

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I'm a 26-year-old British Asian woman, working in media and living in an SW postcode in London.

I've previously lived at two addresses in Sussex and two others in north-east London. While I was growing up, my family lived in a detached house, took holidays to India every year, donated to medical charities, did most of the weekly shopping online at Ocado and read the Financial Times. Now, I rent a recently converted flat owned by a private landlord and have a housemate. I'm interested in movies and startups, have taken five holidays (mostly to visit friends abroad) in the last 12 months and I'm going to buy flights within 14 days. My annual income is probably between £30,000 and £39,999. I don't have a TV or like watching scheduled television, but enjoy on-demand services such as Netflix and NOW TV. I passed through Upper Street in north London every day last week. I can cook a little but tend to eat out or get takeaways often; foreign foods (Thai and Mexican) are my favourite. I don't own any furniture and don't have children. I've never been married.
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I often eat with my university friends on weeknights. I don't care for cars or own one. I dislike any form of housework, and have a cleaner who lets herself in when I'm at work. I shop for groceries at Sainsbury's, but only because it is on my way home. I am not attached to my neighbourhood, and have no contact with my neighbours; I like the idea of living abroad some day. I prefer working as part of a team rather than alone; I'm ambitious and it is important to me that my family thinks I'm doing well. I often go to the pub on Fridays after work. At home, I am far more likely to be browsing restaurant reviews than managing my finances or looking at property prices online. I am rarely swayed by others' views.

This motley set of characteristics, desires, thoughts and attitudes comes very close to defining me as a person. It's also a precise and accurate description of what a group of companies I had never heard of - personal-data trackers - has learned about me.

Earlier this year, I became curious about the personal-data economy. It has grown relentlessly into a multibillion-pound business of tracking, packaging and selling data picked up from our public records and our private lives. As I dug deeper into the world of trackers, it reinforced my anxieties about a profit-led system designed to log behaviour every time we interact with the connected world. I was aware that the data generated by apps and services I use daily - from geolocation and cookies to social-media tracking and credit-card transactions - was building a record of my past.

Combine this with public information such as Land Registry, council tax and voter-registration data, daily location routes and social-media posts, and these benign data sets reveal a lot -- such as whether you're political, outgoing, ambitious, pessimistic, uptight or a risk taker.

Even as you're reading this -- you may be sedentary, but your smartphone can reveal your location and even your posture -- your life is being converted into such a data package; once it has been compiled into lists (interested in technology, subscribes to magazines, probably male, professional, high earner) by intermediaries known as data brokers, it's sold on to data aggregators and analysts and eventually any company.

Ultimately, you are the product.

Under the EU's Data Protection Directive, implemented into UK law in 1998, personal data can be sold to third parties only with your consent and once it has been stripped of your name and any unique identifiers such as National Insurance number. Data is "personal" when third parties are able to link the information to an individual, even if the person holding the data cannot make this link. Simple examples of "personal data" are: full address, credit-card number, bank statements, criminal record etc. Third parties can process the data for their own interests as long as the data subject has consented (this can be assumed, not explicit) and can access or rectify any incomplete and inaccurate entries (Article 12).

Your data on the market: electoral-register details, house prices, driving records, credit-card purchases, NHS hospital data, phone locations and app details.

But particulars such as your postcode, age and gender can be traded -- because they are not personal, but "pseudonymous", defined by the EU Data Protection Regulation (a draft law that has yet to be adopted) as "personal data that cannot be attributed to a specific data subject without the use of additional information".

But the data business has outgrown the directive. With the globalisation of data flows, there is so much additional information available that the risk of "pseudonymous" data being identifiable has multiplied. In the hands of commercial organisations with basic statistics skills, subsets of data can be unlawfully cross-referenced with other data points about you, to identify you with ease.

"Removing someone's name [from a list] to make them anonymous, that's an idea that went out in computer security 15 years ago," says computer scientist Joss Wright, from the University of Oxford's Internet Institute, who focuses on data anonymisation and privacy-enhancing technologies. "If I know where you live and your salary and how many children you have and your medical conditions and your location patterns, then what's in a name?"

In other words, truly anonymising data about an individual is much more difficult than removing their name. In fact, the more data points collected for an individual, the more likely their record is to be unique. According to Pew Research, an average adult Facebook user has 338 friends - that's at least 338 columns or "dimensions" of data per user.

A data set of your mobile-phone locations over an hour will have over 500 dimensions (a phone beams its location to a cellular tower every seven seconds or so); health data, depending on how many types of records you have, could have thousands of dimensions; and genetic data is one-million dimensional (we have about 1.3m genes in total). Once this is matched with any other data set that contains constants such as your age, sex and address, you've been found. In other words, unexpected information can become "personal" when combined with enough other relevant bits of data.

Latanya Sweeney, director of the Data Privacy Lab at Harvard University, has shown that roughly 87 per cent of people in the US can be uniquely identified by the combination of just three facts about them -- zip code, age and sex. Given that Sweeney was referring to a five-digit zip code in the US, which addresses about 320 million US citizens, rather than the 65 million UK citizens serviced by our longer postcodes, this likelihood is far higher for UK citizens.

Sweeney had already proved this was possible in 1997 when William Weld, then governor of Massachusetts, supported the commercial release of 135,000 anonymised medical records of state employees and their families. Each patient was assigned an ID, instead of a name and social security number,and their record contained their gender,zip code and birthdate along with more than 100 fields of sensitive information about prescriptions and treatments at hospitals. For $20, Sweeney purchased the voter-registration records for Cambridge, Massachusetts, containing the name, address, zip code, birthdate and sex of each voter, and cross-referenced it with the anonymised hospital records.

The result: she pinpointed Governor Weld's health records with ease. Only six people in Cambridge shared his date of birth, only three of them men and,of these,only he lived in his zipcode. The linking of health and demographic data is already occurring. The NHS produces a "pseudonymised" set of records called the Hospital Episode Statistics (HES) database. This contains every instance of a patient in England using a hospital-based service since 2001, covering 47 million patients, identified by date of birth,gender and address. "So you might have two databases each with age, postcode and sex included, along with several other features. You can then use those three values to identify the same individual in both data sets," explains Joss Wright.

The UK Health and Social Care Information Centre (HSCIC), which maintains this list, publishes a quarterly register of the organisations with which it has shared the data, including consulting firm McKinsey, BUPA and pharmaceutical firm Astra Zeneca. In July, it made an interesting addition: Experian - a global information-services company that employs 17,000 people in five countries and had revenue of $4.7 billion (£2.8bn) last year; it provides credit-reporting and, marketing services and acts as a data broker, collecting personal information on citizens, companies, vehicles and insurance in the UK, US, Brazil and elsewhere, including historical postcodes, gender and lifestyle habits.

Experian combines the hospital data with its marketing product Mosaic, which provides 66 detailed categories of people living in specific postcodes (O64- my category - is called Bright Young Things. It refers to educated young professionals in urban flats). Because HES data contains a partial postcode and birthdate, plus gender for most patients, this is cross-linked with Mosaic to understand health problems of people in specific postcodes. "In terms of the HES information, pseudonymised data is provided to Experian-this is information that has had key patient identifiers removed," says Experian's spokesperson Nishma Shah. She did not say which demographic identifiers remained.

Although it will be without our names and phone numbers, our mobile-location data is also currently being tracked and sold "anonymously" - but with a unique MAC number attached. "In the last couple of years, an entire industry has developed around monitoring the signals your Wi-Fi radio gives out, and your distinct device can be recognised as you're walking down the street," says Seth Schoen, chief technologist of the Electronic Frontier Foundation. You don't even have to be connected to a Wi-Fi network; your phone just has to be on. Canada-based location-analytics company Viasense uses these Wi-Fi sniffers to pick up your location patterns and MAC number. Viasense tracks the locations of six to ten million devices each day inToronto, and builds lifestyle categories based on owners' movements (people in parks between 6am and 9am are classified as joggers),which CEO Mossab Basir claims it can track down to the square metre.

Telecoms providers such as Vodafone, Telefonica, EE and Verizon have disclosed that they sell anonymised location data (without MAC numbers) packaged into user categories to retailers who want to know their customers' footfall patterns. Free apps are the most effective way for third parties to acquire your locations. "Apps likeFlashlight,Mirror, the gaming apps -- any that ask you for your location are sending it back to the developer continuously, who can sell it to advertisers," says Schoen. "That's why they're free."

In March 2013, researchers from the MIT Media Lab studied 15 months of anonymised locations, donated by an unspecified mobile provider, of 1.5 million people in an unnamed European country. They had customers' location stream on an hourly basis, but didn't know who they were. "We found that we needed just four approximated places and times to uniquely identify 95 per cent of people. About 50 per cent could be identified from just two points," says Yves-Alexandre de Montjoye, first author of the resulting paper and PhD student at the Human Dynamics lab in the Media Lab.

So if you had even four pieces of information that placed someone at a particular place at a certain time - gleaned from social-media posts or public records such as the press - you could untangle their location history in this data set for an entire year. "Even with location information that is a lot less accurate (at a resolution of eight hours, for instance), only a few more points are needed to be able to identify individuals," de Montjoye says. "Your mobility trace is very personal. We found that it's actually easy to identify."

The Snowden files showed that the NSA and GCHQ piggyback on Google's ad-tracking cookies to identify and monitor specific users.

If you think you don't care about being unmasked, you may want to reconsider. Personalised browser ads may be harmless, but allowing disparate aspects of your life to be combined and identified could have unexpected consequences. "The issue is: are decisions, such as whether I get to go to a particular university or whether I end up getting insurance cover, being made about me using data I never intended to give to a third party - for example, my lifestyle or a family member's ailment?" says Tim Sparapani, privacy lawyer at the Apps Alliance and ex-director of public policy at Facebook. "This is not about the future -it's already starting to happen."

Earlier this year, Cambridge University security engineer Ross Anderson found that NHS hospital data had been sold to, among others, the Institute and Faculty of Actuaries, which has used the information to calculate patients' risk of developing critical illnesses, and so "improve the accuracy of pricing" (read: increase insurance cost). "The Department of Health was selling off every NHS hospital patient's records for insurance purposes," says Anderson.

"They claimed it was non-sensitive because it had been anonymised, but that's false." The institute's spokesperson, Annette Heninger, confirmed the data sent over." The IFoA paid
£2,200 as a processing fee for the HSCIC to provide the data. It contained the first part of the postcode, gender and age of every patient at the start and end of hospital admission (no dates of birth) and up to 20 different categories of diagnosis." The actuaries cross-referenced this data set with lifestyle information on patients' postcodes from Experian's Mosaic database. They found that levels of critical illness among most customers below the age of 50 were higher than previous calculations had found. As a result,insurers were likely to raise insurance premiums for this group, according to Anderson. "We let the facts and the numbers speak for themselves," says Heninger, from the institute. "Any member of the public is able to... make their own decisions about the research and the implications of that research."

Although this revelation caused a furore in the press, the NHS decided to push ahead with the next phase of its open-data plan - the release of all NHS England's GP data in a similar form to the hospital data, in a data set called Care.data. The NHS pledged to inform citizens by writing that their data would be made public; leaflets did not always reach their targets. "Most of it was thrown into junk mail, and we had to meet with the Secretary of State to ask for it to be an opt-out process," says Sam Smith, cofounder of UK-based medical privacy non-profit MedConfidential.

The leaflet reads: "We will use information such as your postcode and NHS number to link your records from these different places [GP practice, hospitals and community services]. Records are linked in a secure system so your identity is protected." It also states: "We sometimes release confidential information to approved researchers, if this is allowed by law and meets the strict rules that are in place to protect your privacy.

"The data will include referrals, diagnoses, NHS prescriptions, your family history, vaccinations, blood-test results, BMI, smoking/alcohol habits etc - dating back to April 2013. The information - which will exist for every person who uses the NHS in England - will be extracted as a series of codes, not as words or sentences. "This granular data set would include linked data of every time any individual in the NHS went to their GP," says Smith. "That's basically all the medical history for everybody in England, forever."

Identifiers such as date of birth, postcode, NHS number and sex are required in order to link the GP data with the data from hospitals. But these details will be modified for privacy reasons when linked to other data sets, and the identifiers will be stored separately, according to the HSCIC. Critics believe that HSCIC retains, at the very least, a look-up table for these identifiers. Because this is a linked, long-term data set pertaining to individuals, patients are very vulnerable to being identified.

"We were told this data would be anonymised. This is a really stupid thing to say. Really stupid. Because the degree to which you can anonymise data is in inverse proportion to its usefulness. Almost exactly," Conservative MP David Davis tells WIRED. "I have broken my nose five times and I'm 65. Once you know that, you've reduced the pool to about 100 people.
Then you check the data for when I had my diphtheria jab, usually done at birth, and that's it, you got me."

Both the HSCIC and NHS claim that Care.data won't be shared with commercial organisations and will only be used for research purposes. But in April this year the HSCIC publicly disclosed that HES data had been given (it claims fees were for processing the data) to over 1,000 firms. Of these, at least five, including IT company OmegaSolver, were given a commercial-reuse licence, according to Sam Smith. "This means they can sellthe data on," he says. NHS data was used by OmegaSolver to help pharmaceuticals sell their drugs. The company produces a dashboard that visualises the medical history of 163, 316 patients from the HES dataset.

An example on its website captured by MedConfidential shows female patient OS060900, aged 81-85, who had five conditions diagnosed in October 2010. She had 257 hospital visits, mostly as an outpatient, but also had a five-day stay at which point eight conditions were diagnosed. If OmegaSolver had her postcode too, it could easily match this with electoral records for her. The company declined to comment.

Care.data was put on hold in February, but will be restarted soon across roughly 500 GP practices, according to NHS England. Tim Kelsey, NHS national director for patients and information, was unavailable for comment.

American bank Capital One is using personalised data to decide which financial products to show first-time customers. It uses the services of New York-based data-tracking firm [x+1], which claims it can determine personal details (not names) of a website visitor within 200 milliseconds.

According to cofounder Ted Shergalis, it analyses information about users' devices, browsers and location through IP addresses; it also buys postcode data and profiles about users' hobbies and interests from online tracking companies and data brokers.

In 2010, the Wall Street Journal approached [x+1] for a piece on how its customer profiling worked. Capital One allowed [x+1] to identify two volunteers -- Thomas Burney, with no children, a graduate who worked in management, owned his home; and Carrie Isaac, a young small-town mother with a midscale income. The Journal's exposé showed how, based on [x+1]'s assessment, Capital One promoted to Isaac some of its least generous cards, while Burney saw only one, the Capital One Prestige Platinum, which included no initial interest or annual fee. Both participants had assumed they were offered the only available options, and were unaware of the tailoring.

During an average day, I wake to my phone, fumble for it on the bedside table and take in the data dump. Today, there are 12 emails, three tweets, four Facebook notifications, two Endomondo prompts and a reminder about a meeting.

The data deluge is everywhere I go - a row of 12 recycling bins with display screens are lined up near Bank station; a screen in the forecourt of a Tesco in Southwark advertises products all day; the Godiva chocolate shop on Regent Street arranges its window display as a form of advertising. Every step of the way, strangers are tracking what I do and drawing a richly detailed profile. When I log data into Endomondo, the app accesses my sex, location and date of birth to share with advertising company DoubleClick, according to cofounder Mette Lykke.

Other health apps do it too: according to independent research conducted for WIRED by Princeton University researcher Ian Davey, WebMD for iOS leaks a large amount of potentially sensitive medical data. When a user views pages for specific drugs, conditions or procedures,the identifiers and sometimes names of what was viewed (along with a unique phone identifier) go to Scorecard Research, Nativo, Adobe Omniture and AOL's subsidiary Gravity. "Any data is subject to confidentiality obligations and can only be used by these contractors to provide services to WebMD," says spokesperson Catherine Daniels.

One of the largest data brokers, Acxiom, which works with Facebook, has data on over 750 million consumers, with up to 1,500 data points on each

As long as my phone is on, my movements can be tracked. Until August 2013, the bins at Bank tube station were picking up phones' unique MAC numbers via their Wi-Fi, without the knowledge of phone owners - a technology built by London startup Presence Orb and implemented by Renew London. While it was tracking passers-by, Renew - now in administration - would know if I was the same person who passed by yesterday, my specific route and how fast I was travelling.

That's exactly what the sensors in the Godiva shop do. ShopperTrak, a Chicago company, counts the people passing by Godiva via their phones, and modifies the display accordingly. According to Russell Evans, VP of global marketing, ShopperTrak also uses in-store Wi-Fi sensors to track customers' phones, so it knows if they came back.

The screen in Tesco's forecourt has a camera installed by London-based Amscreen, as it has in 500 stores around the UK. "The screens are not just passive TVs -- they are like giant mobiles with sim cards, 3G and GPS. The algorithm, made by French company Quividi, knows someone is standing in front of it and figures out your sex and age, while also recording the time and location," says Mike Lemmings, head of marketing and product development at Amscreen. Demographic patterns can then be sold to advertisers. Tesco now knows I am a 26-year-old female and, if combined with my Clubcard data, it could find out what I buy and where I live, as well as my demographic details.

I decided to piece together what these companies really knew about me. I spoke to Eyeota, a data-analytics profiler with offices in Singapore, Berlin and Sydney. It has -partnerships with a range of websites, so when I visit their pages, it can place a cookie on my browser. It also buys data from Experian to enhance each cookie profile. Using my browsing activity, Eyeota assigns my cookie up to a thousand attributes ranging from sex and region to type of job, whether I have children, own a car, like to buy Star Wars -- memorabilia and so on. It never finds out my name, but it knows more about me than my neighbours do.

My Eyeota cookie knows that I'm a 26- to 35-year-old female, working in the media/internet industry.

I'm interested in entertainment, particularly movies, and in entrepreneurship and startups. I intend to buy flights in the next 14 days.

Eyeota also buys data from Experian's Mosaic database: a collection of 15 demographic groups and 66 lifestyle types based on your postcode and calculated from a variety of data sources which Experian would not disclose (it could mention only voter-registration records and census records as examples). It matches you up to a type.

My Mosaic type runs to 16 pages, with predictions about everything from my financial circumstances to my view on the world. It includes an accurate description of my ethnicity, age, education, profession, home life and financial circumstances.

Because Eyeota buys this profile, it knows I probably take taxis rather than buses when I get home late, that I am unlikely to visit DIY stores, that I spend a large part of my income on eating out and long-haul, use sites such as Airbnb, and that most of my friends are people I met at university. It can then sell this information to the highest bidder.

Profiling individuals has been a gold rush. "It expanded post-9/11 because governments were trying to prevent the next 9/11," Sparapani says. "This was all to try to predict who is related to whom, engaged in terrorist activity, or laundering money. A new ecosystem was established." Now, the data is traded commercially and sustained by advertisers -- the Interactive Advertising Bureau says revenues in the US last year hit an all-time high of $42.8bn. A large part of it is based on selling targeted user data on Facebook and Google. A range of more sensitive data is now available, leaving us much more exposed.

They're going to know more about us than we know ourselves.

Now our tax records could be exposed in a similar way to our HNS records. "[In July], we had a meeting in Parliament about a proposal from HM Revenue and Customs to sell our tax records to people in the City and remove names and addresses from it but assign a unique ID," Ross Anderson says. Such a plan to sell off anonymised tax records is described as "borderline insane" by David Davis. "If they're [like] the NHS, the unique ID might be postcode. If all the tax records at a postcode are lumped together, there are only a few houses it could be," says Anderson.

HMRC has already launched a pilot, releasing data, such as names and addresses of companies that are VAT registered, to three credit-ratings agencies, including Experian. "It's really hard to see how this is anything but commercial, frankly. [Companies] are going to make value by adding together tax records, social records, existing databases and health records. They're going to know more about us than we know ourselves," says Davis. "This is statewide identity theft."

And the more data that becomes available, the easier it will be to identify you. "All biometric sensor data sources are going to be pretty easy to re-identify," says Scott Peppet, privacy lawyer at the University of Colorado. "Think about your heartbeat or how you walk, or your pattern of exercise. There's gonna be no one who has the identical patterns to you."

In June 2013, the CIA's chief technology officer Ira Hunt gave a talk at GigaOM's Structure:Data conference in New York about the importance of wearables such as the Fitbit to the security services. He said not only could you infer an individual's sex, height and weight from Fitbit data, but that they were "100 per cent guaranteed to be identified simply by [their] gait".

When contacted by WIRED, Fitbit representative Katie Henry said, "We use Mixpanel (and other analytics providers) to understand how customers use Fitbit. Mixpanel, just like all our third-party service providers, is prevented from using any personally identifiable data for any other purposes."

As the data we generate about ourselves continues to grow exponentially, brokers and aggregators are moving on from real-time profiling -- they're cross-linking data sets to predict our future behaviour. Decisions about what we see and buy and sign up for aren't made by us any more; they were made long before. The aggregate of what's been collected about us previously -- which is near impossible for us to see in its entirety -- defines us to companies we've never met. What I am giving up without consent, then, is not just my anonymity, but also my right to self-determination and free choice. All I get to keep is my name.

This article was first published in the November 2014 issue of WIRED magazine

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Madhumita Venkataramanan: My identity for sale

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I'm a 26-year-old British Asian woman, working in media and living in an SW postcode in London. I've previously lived at two addresses in Sussex and two others in north-east London. While I was growing up, my family lived in a detached house, took holidays to India every year, donated to medical charities, did most of the weekly shopping online at Ocado and read the Financial Times.

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Vavik om motivasjonskonflikter

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Statsminister Erna Solberg har et poeng når hun sier at "de dårlige norske prestasjonene i matematikk skyldes tiår med lite satsing på faget".

Det vi i Norge er mest kjent for i denne perioden er storsatsing på IKT i skolen. Ingen andre land har digital kompetanse som en grunnleggende ferdighet ved siden av det å regne, lese og skrive. Ingen andre har et eget kompetansenettverk for IKT i skolen, som tester lærernes og elevenes "digitale tilstand" og gir påbud om å være digital i alle fag hver dag.

PISA-undersøkelsene viser også at norske 15-åringer har best tilgang til datamaskiner i OECD-området. Flere elever i Norge enn i andre land bruker Internett til delta på sosiale medier og surfe på nettet.

Sammenhengen mellom IKT i undervisningen og faglig utbytte er uklare. Går man PISA-tallene nærmere etter i sømmene, kommer det fram flere problematiske forhold. I 2006 sto det i Der Spiegel: "Jo mer elevene bruker datamaskinen, desto dummere blir de".

I 2013 viser det seg at en av skolene som har satset på realfag og intensiv bruk av IKT har dårligere resultater enn både lands- og fylkesgjennomsnittet i samtlige realfag. Derfor er det ikke nødvendigvis negativt at kompetansesenteret for IKT i skolen viser til at bruken av IKT har gått noe ned i Norge de siste to åra.

Digitale verktøy kan gi en merverdi også i undervisningen i matematikk. Det har jeg dokumentasjon på etter å ha veiledet femti praktiserende lærere under mastergradsarbeidet i IKT og læring. I en undersøkelse av de lærerne som år etter år oppnår gode resultater med sine klasser, oppgis disse forklaringene:

1. Jeg har svært god utdanning i faget.
2. Jeg har evnen til å inspirere elevene.
3. Jeg bruker forskjellige hjelpemidler, både analoge og digitale, til rett tid og på rett plass.

Starter vi med hjelpemidlene før de andre forutsetningene er til stede, blir resultatet noe helt annet.

Når elevene har åpent Internett, har de i praksis tilgang til en svært mangesidig verden av digitale verktøy og aktiviteter.I et stort forskningsprosjekt, finansiert av Norges forskningsråd, spør vi blant annet om hvordan skolen bør forholde seg til elevenes digitale verdener. Både Finland og Norge er med i den første undersøkelsen. Etter å ha analysert 4000 elevsvar ser vi noen interessante trekk:

En stor prosentdel av elevene i Norge sier at de mister fokus på skolearbeidet når de bruker digitale hjelpemiddel på skolen. Dette er den faktoren hvor forskjellen er størst mellom Norge og Finland.

Det rapporteres i PISA-undersøkelsen at norske elever har lav utholdenhet for å jobbe med matematikk - lavere enn gjennomsnittet i OECD og lavest i Norden. Mange av disse elevene finner vi igjen i vår undersøkelse. Her er et utdrag fra et fokusgruppe-intervju med elever på niende trinn:

"I timen så sitter jo de fleste og ser på Facebook og sånne ting."
"Vi sitter mye og spiller i timene."
"Læreren kommer inn og sier: Nå skal vi ha matte. (...) Det er jo dritkjedelig. Da hører jeg på musikk, YouTube og Spotify."

Eksempelet viser hvordan fritid og skole smelter sammen i teknologitette klasserom. De ansvarlige for den norske IKT-satsingen så i utgangspunktet ikke noe problematisk ved denne situasjonen, snarere tvert om.

Nå opplever også Finland en faglig tilbakegang. Finske elever i niende klasse ligger nå "bare" ett år foran norske elever på resultatlisten i matematikk. Årsakene er selvsagt sammensatte, og det blir for enkelt å legge skylden på elevenes bruk av digitale medier alene. Likevel blir det naivt å se bort fra en forklaringsfaktor som også finske elever trekker fram gang på gang. Se hva elevrådsformannen på en av de finsksvenske skolene svarer læreren som forundrer seg over at resultatet ikke er så bra som det engang var:

"Julen 2011 fick många elever till julklapp smartphones och det har börjat inverka negativt. Istället för att lyssna på hur man får kvadratroten ur talet 13,5, väljer jag ett spel på sin iPhone. Själv sätter jag hörlurar i öronen om jag inte orkar lyssna på läraren. Det blir en ond cirkel som är svår att komma ur."

Før var elevenes fravær synlig ved at kroppene ikke var i klasserommet når klokka hadde ringt. Også den gangen hadde vi våre mentale fluktruter. Men i dag er distraksjonene nærværende og lett tilgjengelige over alt og hele tiden, og er av et helt annet livsstilskaliber enn tidligere. Det er svært lett å være "fraværende til stede" via sin egen pc eller mobil.

Underholdningsindustrien utvikler svært så effektive virkemidler for å fange unges oppmerksomhet. Tiden som brukes på digitale medier står i kontrast til det langsiktige læringsarbeidet i skolen. Mange elever opplever en motivasjonskonflikt mellom oppgaver som krever faglig utholdenhet og det umiddelbart tilfredsstillende ved oppdatering på Facebook, svare på venneforespørsler og meldinger, og delta i nettbaserte spill. De føler seg fanget mellom to verdener.

I motsetning til i Finland har norske elever lang erfaring med teknologitette læringsmiljø, og har i mange sammenhenger fått stor grad av frihet. Dette har også blitt framstilt som en idealsituasjon av dem som var ansvarlige for den norske IKT-satsingen. Det er et tankekors at elevene med mest erfaring, er de samme som nå ber skolen innstendig om hjelp til å tenke gjennom verdien av sine digitale aktiviteter og vaner.

Det er forskjell på lærernes status og relative lønn i de land vi sammenlikner oss med.I Finland er lærerutdanning vanskeligst å komme inn på, deretter kommer medisin. Du får først praktisere som lærer etter at du har tatt mastergrad.

Norske elever fortjener å lære mer matematikk gjennom de ti åra de går i grunnskolen. Det kreves faglig sterke lærere som er sikre i matematikkfaget, og som er gode til å vise hver enkelt elev hvorfor matematikk er relevant for nettopp dem. Det er nettopp her vi kommer aller nederst på stigen av alle land som var med i PISA-undersøkelsen.

Elevene mener at læreren ikke forklarer begrepene godt nok og bidrar heller ikke til å skape interesse for faget. Dersom dette svikter, sammen med lav utholdenhet til å jobbe med matematikk og høy aktivitet på digitale medier, har vi en svært så uheldig kombinasjon.

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Statsminister Erna Solberg har et poeng når hun sier at "de dårlige norske prestasjonene i matematikk skyldes tiår med lite satsing på faget". Det vi i Norge er mest kjent for i denne perioden er storsatsing på IKT i skolen. Ingen andre land har digital kompetanse som en grunnleggende ferdighet ved siden av det å regne, lese og skrive.

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The more things change the more the remain the same.....

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The more things change the more the remain the same.....

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Vavik og Arnesesns artikel om ungdomskulterens møte med skolekulturen

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Theories for learning with emerging technologies 

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Theories for learning with emerging technologies 

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Om læringsteorier knyttet til teknologier brukt i læring