#Schoolprivacyzone: emerging best practices for a contentious issue

Advocacy group Common Sense Media held a summit in Washington, DC on Monday as part of a national campaign on the highly contested topic of student data privacy.

The results of Common Sense Media's January survey show most parents concerned about student privacy.

The results of Common Sense Media’s January survey show most parents concerned about student privacy.

A recent study by Fordham University Law School found that as schools and districts adopt cloud computing services, they are transferring student information to third-party providers, often leaving it open to data mining and commercial purposes such as reselling and ad targeting. These services may be in violation of federal law. These agreements allow vendors to do whatever they want with student demographic records and other personal information. Of the districts studied, fewer than 25% of the agreements between districts and vendors specified the purpose for disclosures of student information, and fewer than 7% restricted the sale or marketing of student information by vendors in any way. And that is to say nothing of the risk of hacking or other security breaches.

There are a number of attempts in the works to establish better guidelines for the $8 billion educational software industry. The Software&Information Industry Association, a trade group, yesterday announced a list of best practices for agreements between software groups and schools:

That data should be used only for educational purposes, that its use should be fully disclosed and transparent and full consent obtained from families, that all reasonable security procedures should be followed and schools be notified in case of actual data breaches.

Even as the industry is taking baby steps to govern itself, lawmakers are converging on a solution with more teeth. California State Senator Darrell Steinberg just introduced a bill in that state enforcing some of these same principles: educational purposes only, encryption and deletion of data. Massachusetts Senator Ed Markey plans to do the same at the federal level.

At the Common Sense Media event, according to the lively discussion on Twitter, industry representatives like Cameron Evans of Microsoft and Joel Klein of Amplify argued that a rush to legislate might cause more problems than it solves. Best practices for data privacy and security continue to evolve as the technology does. The large-scale use of cloud computing and web-based data storage itself dates back only to the mid-2000s. It is difficult for the law to catch up. Also, while contracts may specify “educational purposes only,” the nature of the beast in ed-tech is that a large source of educational innovation is coming from for-profit startups whose involvement with the day-to-day experience of teachers and students is becoming increasingly intimate, if not intrusive. In practice the line between educational and commercial purposes may be somewhat blurry. As Katherine Varker, Associate General Counsel, McGraw-Hill Education, asked at the summit: ‘Where does targeted advertising end and personalized learning begin?’


Coursera founder phones it in at open education conference

Coursera Infographic

#OpenEd2013 is the tenth annual installment of the premiere conference of the open education community, taking place right now in Utah. Open education is currently contested territory, with divisions highlighted yesterday by a flatfooted keynote from Andrew Ng, cofounder of Coursera, that played out to a baffled chorus of mockery on Twitter. Amid the jibes, there’s a serious issue at stake: will the future of education be dominated by a few closed platforms, and limited approaches to teaching, learning and knowledge, or will truly open innovation prevail?

Hope someone warned Ng that he can’t toe the standard Coursera line for #OpenEd13 talk. They have been doing this a lot longer than he has.— Amy Collier (@amcollier) November 6, 2013

Open education was first most closely identified with OER–digital educational resources such as MIT’s Open Courseware that carried an open license, such as the Creative Commons license, allowing them to be freely shared, reused and remixed. For self-identified open and connected educators, though, mostly from the higher ed world, openness wasn’t just a technical designation. They were concerned with democratizing education, making it accessible to all, peer-driven rather than hierarchical, emphasizing the fluid process of learning rather than the rigid gateways of accreditation–“an exploratory, community-created knowledge building process,” in the words of Athabasca University professor George Siemens. In this spirit, Siemens and Stephen Downes ran the first Massively Open Online Course, or MOOC, in 2008, with about 25 University of Manitoba students joined by 2500 students online. The topic–a bit meta– was “Connectivism and Connected Knowledge. ”

Today, of course, the term MOOC means something very, very different. From experiments pursued by a small group of learning and teaching enthusiasts, a handful of platforms — edX, Udacity, and Coursera— have emerged with tens of millions of dollars in backing from venture funders and foundations, hundreds of university partners, and millions of users. There is a dominant format for the MOOCs published by these platforms: they run from six to 14 weeks long, and consist of short video lecture “chunks” presented often by well-known professors, interspersed with multiple-choice comprehension questions, combined with readings, often homework assignments or an exam, and forums for discussion.

–I’m hoping Ng’s keynote is actually a bunch of short videos with intermittent quizzes. #opened13— Jonathan Becker (@jonbecker) November 6, 2013

Most of the MOOCs, while free to access currently, are not open-licensed–they are the intellectual property of the companies and institutions and thus can’t be downloaded, reused, or remixed freely.

–Why isn’t Coursera openly licensed? Ng says that its content creation costs too much money and that wouldn’t be sustainable #opened13 (sigh)— Audrey Watters (@audreywatters) November 6, 2013

Ng is the quieter of Coursera’s two cofounders. He’s also director of the Stanford Artificial Intelligence Lab, which means he has a deep intellectual interest in the growing field of “educational data mining,” learning research, training computers to grade essays and tracking student engagement. Coursera, like other large MOOC platforms, offers the opportunity to learn a great deal about the learning process, at least as it plays out online.

@jonbecker our analytics have determined your diminished interest and have now dispatched mentors to keep you engaged. #opened13— George Siemens (@gsiemens) November 6, 2013

His keynote, however, failed to address these research questions, and instead delivered a standard pitch about Coursera to people who are already quite aware of what it is. Also, unfortunately for a presentation on hybrid learning, there were technical problems.

–Oh the irony. Andrew Ng is Skyping in for his keynote at #opened13. So we’ve been asked to turn off devices to save bandwidth. MOOOOOOC!— Audrey Watters (@audreywatters) November 6, 2013

The irony is worth underlining: the OpenEd community, whose major criticism of MOOCs is that they enshrine the one-way, rigid lecture format, was asked not to respond via the open web while Ng was lecturing to them over a video link.

Within the open education world, as summarized by George Siemens’ keynote right after Ng’s, there are a range of feelings about MOOCs–both angst and hope. This is not just a group of hipsters who are upset that their favorite band suddenly got really popular, or merely professors angry that someone is turning their life’s work into a business.

–Sorry Dr Ng, but there are heaps of people who create engaging online learning inexpensively. Too bad you couldn’t meet them at #opened13— Brian Lamb (@brlamb) November 6, 2013

These are engaged, excited, experimental educators and learners, with values that they fear are getting lost as MOOCs get even more massive. They want their due as partners in the creation of a diverse and vital future of education.

@GardnerCampbell Ng’s talk had no sense of or much regard for its audience. Conversation would be great, but there’s a sense + #opened13 in which xMOOCers refuse to meaningfully engage thoughtful critiques that was symbolized by what went down #opened13 — Luke Waltzer (@lwaltzer) November 6, 2013

As the K-12 “connected educator” movement grows, this debate will be increasingly relevant across all levels of education. Do we want a future where mass market MOOCs and similar digital resources are primarily prepackaged and delivered to students via a vendor-like, consumption-based model? One that enshrines the several-week course and the talking-head lecturer as the central model of education? Or will more messy, diverse, participatory models of open education have the opportunity to spread and take root? Can the two approaches interact and maybe even reinforce each other?

This was clearly a missed opportunity to raise these questions and more.

–I’m dropping out of this keynote. #opened13— Jonathan Becker (@jonbecker) November 6, 2013





An automatic boredom detector? Inside “educational data mining” research


I’m currently working on a book about the past, present and future of assessment. For the “future” bit I get to talk to researchers like Ryan Baker at Columbia. He’s spent the last ten years working on systems that gather evidence about crucial parts of the learning process that would seem to be beyond the ken of a non-human teacher.

The basis for the observations comes from what’s called “semantic logs” within a computer learning platform, such as Khan Academy’s: Was it a hard or easy question?  Did the student enter a right or wrong answer? How quickly did they answer it? How did it compare with their previous patterns of answers? The detectors gather evidence that students are gaming the system, drifting off-task, or making careless errors. They can extrapolate a range of emotional states, like confusion, flow, frustration, resistance, (which Baker calls memorably “WTF” behavior), engagement, motivation, excitement, delight, and yes, boredom.

Baker’s engagement detectors are embedded within systems currently being used by tens of thousands of students in classrooms from K-12 up to medical school. (Medical residents, he says, show the highest rate of “gaming the system,” aka trying to trick the software into letting them move on without learning anything, at rates up to 38% for a program that was supposed to teach them how to detect cancer.) His research, located at the forefront of the rapidly expanding field known as “educational data mining,” has a wide range of fascinating applications for anyone interested in blended learning.

Understanding how good these detectors currently are requires a bit of probability theory. To describe the accuracy of a diagnostic test, you need to compare the rate of true positives to the rate of false positives. The results for the “behavior detectors,” Baker says proudly, are about as good as first-line medical diagnostics. That is, if the question is whether someone is acting carelessly, off task, or gaming the system, his program will be right about as often as an HIV test was in the early 80s–0.7 or 0.8 (“fair” according to this rubric). For emotional states, which require a more sophisticated analysis, the results are closer to chance, but still have some usefulness. These accuracy scores are derived from systematic comparison with trained human observers in a classroom.

So why would someone want to build a computer program that can tell if you are bored?

To improve computer tutoring programs. Let’s say a learning program provides several levels of hints before the right answer. You want to build something in that prevents a student from simple gaming techniques, such as pressing “hint, hint, hint, hint,” and then just entering the answer.

To give students realtime feedback and personalization.  “I would like to see every kid get an educational experience tailored to their needs on multiple levels: cognitive, emotional, social,” says Baker. Let’s say the program knows you are easily frustrated, and gives you a few more “warmup” questions before moving on to a new task. Your friend is easily bored. She gets “challenge” questions at the start of every session to keep her on her toes.

To improve classroom practice. Eventually as these systems become more common, “I would envision teachers having much more useful information about their kids,” says Baker. “Technology doesn’t get rid of the teacher, it allows them to focus on what people are best at: Dealing with students’ engagement, helping to support them, working on on one with kids who really need help.” In other words, though technology can provide the diagnostics for affective states that affect learning, it is often teachers that provide the best remedies.

To reinvent educational research: This is a fascinating one to me. 

“I’d like to see educational research have the same methodological scope and rigor that have transformed biology and physics,” Baker says. “Hopefully I would like to see research with, say, 75% of the richness of qualitative methods with ten times the scale of five years ago.”

Modeling qualitative factors related to learning opens up new possibilities for getting really rich answers to really interesting questions. “Educational data mining often has some really nice subtle analyses. You can start to ask questions like: What’s the difference in impact between brief confusion and extended confusion?”

In case you’re wondering, I will clear up the confusion. Brief confusion is extremely helpful, even necessary, for optimal learning, but extended confusion is frustrating and kills motivation.

The very phrase “data mining” as applied to education ruffles feathers. It’s helpful to hear from an unabashedly enthusiastic research scientist, not an educational entrepreneur with a product to sell, about this topic. Privacy, he says, should be given due consideration. “The question is what the data is being used for,” he says. “We have a certain level of comfort with Amazon or Google knowing all this about us, so why not curriculum designers and developers? If we don’t allow education to benefit from the same technology as e-commerce, all we are saying is we don’t want our kids to have the best of what 21st c technology has to offer.”

If you’re interested in learning more, Baker has a free online Coursera course on “Big Data in Education” starting this Thursday. Over 30,000 people have signed up.

Can online learning make teaching more human?

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Data-driven pedagogy. The phrase conjures a robotic, dull future that only intensifies the worst aspects of 20th-century, bureaucratic, industrial wasteland-style schooling, where learners are defined down to “users,” or even metonymized as disembodied “eyeballs,” and force-fed bits of disconnected information.

For a counternarrative, the question is simple. What can creative humans do with the power of data? One possible answer is that computer-powered analytics could expand humans’ ability to focus on the most human aspects of teaching and learning.

I reported earlier this year on a small experiment the video website Khan Academy ran to this end.

While browsing the web site, some Khan users saw a simple slogan added to the page next to, say, a math problem: “The more you learn today, the smarter you’ll be tomorrow.” The line linked to a further explanation of the concept of “mindset,” the famous body of research by Harvard psychologist Carol Dweck on growth, achievement and motivation.

Displaying that one line led to a 5% increase in problems attempted, proficiencies earned, and return visits to the site, compared to otherwise similar learners who did not see the line.

This week, Andrew Liu, Udacity’s data science intern, blogged about his own research with the data generated by that MOOC platform. Apparently the questions they are framing go along similar lines: toward psychological aspects of motivation and engagement.

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Model of student engagement over time.

Modeling student engagement over time.

“At Udacity, we now have the opportunity to take findings that originated from studies on tens of students in physical classrooms – such as Carol Dweck’s concept of growth mindset – and apply learnings to hundreds of thousands of students with improved teaching. But even more powerful is Udacity’s ability to conduct our own pedagogical research at scale on a rapidly growing worldwide classroom that was not even possible a year ago. Pedagogical areas we’re exploring include the importance of metacognition, expectation setting around formative assessment, and even new online challenges such as which characteristics of video keep students most engaged.” 

Mindset, metacognition (learning about learning), engagement–these are great research questions for educators to be looking at. They are not chiefly about automating the consumption and digestion of information, but about deepening the learner’s physical and emotional relationship with the process of learning.

It’s in part simply the growth of sample sizes that has some researchers so excited about what they might learn in the emerging field of data-driven pedagogy. I haven’t verified this independently, but I have often heard researchers repeat the notion that there are just very few large-scale randomized controlled trials out there comparing the efficacy of various classroom techniques and methodologies.  Sample sizes tend to be quite small and experimental effects hard to compare. (If there are counterexamples, I’d love to hear them).

A major example is the efficacy of online and blended learning itself. According to a comprehensive literature review published by Ithaka SR earlier this year, of over 1000 online and blended learning studies reviewed by the US Department of Education, only 45 met minimal criteria of having experimental research design and considering objective learning outcomes. Of those 45 studies, “most have sample sizes of a few dozen learners; only five include more than 400 learners.”

The kind of a/b trials that the Khan Academy and Udacity are doing, by contrast, can be easily run on hundreds of thousands of people.

Obviously there are relationships and aspects of the human dimension of learning that can’t be addressed with even the best data tracking and experimental design, or the largest sample sizes. There is an ever-present danger that the metrics chosen will tend to distort the nature of the undertaking itself. However, I can’t help but be a little optimistic that at least data scientists are starting with the right kinds of questions.

InBloom is wilting thanks to privacy concerns–but they don’t stop with InBloom


In my first post for this blog I covered the splashy debut of InBloom at the SXSWEdu conference in Texas in March. I noted that it’s tough to explain exactly what the company does (essentially, they provide the infrastructure for a variety of smaller applications to harness the data generated by students to make their offerings more efficient and personalized). I also highlighted privacy concerns that are starting to surface about the collecting, repackaging and re-selling of student data for the benefit of for-profit companies.

Several months later it seems that both the inability to explain for the layperson what the company does, and the panic over privacy and security (underlined by the recent upheaval over NSA data mining), are dogging InBloom and may doom it. The number of partner and pilot states for the organization, initially listed at seven, is now down to five according to the website. And in at least two of those states, New York and Colorado, the idea faces vociferous local opposition. The American Federation of Teachers has stepped in, issuing a statement citing a “growing lack of public trust” in the company.

This debate is important, and as the AFT notes, it doesn’t stop with InBloom. The promise of big data for schools is not going away and neither are the perils, so perhaps it’s time to have a more grounded conversation about both the issues and the remedies at hand.

Recently I spoke with entrepreneur Jose Ferreira of the adaptive learning platform Knewton, another kind of big data company in education. During our conversation he said that from the ed-tech point of view, there are several types of student data. Each has different values and different dangers. (Ferreira separated out five kinds of data in his typology, but to simplify I’ll designate just three).

1) The first type is known as personally identifiable information: names, addresses, Social Security numbers. Exposure of this data generally is a security breach of the first order. It may be valuable for spammers, but it’s not all that useful to analyze for educational outcomes. Say you find out that girls named Alana from Phoenix do better in reading–that’s not generalizable. For this reason PII should always be well hidden and inaccessible.

2) The second type of data is the kind collected and tabulated by school, state and federal student information systems–let’s call it SIS. There is academic and behavioral information, like attendance, standardized test scores, suspension rates and class sizes. And there’s demographic data, like ethnicity, learning disability or IEP classification, and the percentage receiving free/reduced lunch. It’s very useful to correlate this kind of data with educational outcomes and interventions. It’s necessary for resource allocation. Because it pertains to groups, not individuals, it’s less sensitive than PII. But there’s still a chance for schools or groups to be stigmatized or stereotyped with the sharing of such information, so it needs to be released judiciously. No one is arguing that a particular student’s test score, for example, should be a state secret, but as with anything that appears on a transcript, its release should be controlled and limited to those who need to know. 

3) The third, and newest, type of data is the user interaction information collected by learning software systems like Dreambox, Khan Academy or Knewton. These systems record time on page and keystrokes and combine them with student responses to assessment questions to construct a picture of the engagement and proficiency of individual students and the efficacy of particular pieces of content. This is where you truly get into “big data.” Some of these systems claim to generate millions of data points per hour.

Let’s leave PII alone for a minute. The power of both SIS and “big data” to improve the practice of teaching and learning depends on aggregating and analyzing as much of it as possible, and making the relevant results available as quickly as possible to students, educators, parents, and the people who build these systems. The system we have today doesn’t do a very good job of this. Adequate Yearly Progress test results, for example, typically become available several months after a student takes the test. If big data is going to be useful at all, the privacy considerations attached to it have to be different because of the sheer volume and velocity at which it is generated. “Opting-in” often becomes impractical.

I would suggest separate tests be applied to determine responsible privacy and security considerations for student data. PII should always be separated out and kept hidden except when explicitly shared or agreed to by informed individuals. SIS and “big data” should be protected and its use disclosed, especially when it’s being made available for the enrichment of private businesses. (For example, I’m not a huge fan of the startup Junyo, founded by a former cofounder of the online game company Zynga, which has introduced a product that scrapes publicly available SIS data and sells the information to textbook and ed-tech companies for marketing purposes).

In all cases, we have to balance the potential harm to vulnerable young people with the potential gains to learning and teaching.



Bill Gates greeted by standing ovation from…teachers?

Patti Freudenberg works for the Clear Creek Independent School District in the Houston-Galveston area, where she is the “Teaching American History Grant Specialist,” currently leading groups of history teachers to historical sites around the US on a Department of Education grant. Clad in leopard print, she arrived bright and early Thursday morning to sit in the very front row of Bill Gates’ closing keynote at the South by Southwest Edu conference. “It’s a hero worship thing,” she said. “I don’t know what he’s going to say. Hopefully he’ll talk about new ways we can use technology. Because education is changing so much–we’re finally moving from the traditional classroom to something that’s more technology rich and media rich. We’re reaching these kids where they actually are instead of what we had in the 1960s.” She’s seen this in her own schools, where pilot programs are bringing iPads into the classroom, making it easier to incorporate more primary source documents and multimedia into social studies classes.

Gates’ wide-ranging keynote, greeted by a standing ovation, didn’t disappoint Patti. He argued that the market for educational innovation is reaching a tipping point–although he acknowledged that from an investment point of view, this is simply a return to the heights of the late ’90s, when annual investment in ed-tech once topped $1 billion just as it did again in 2012. The first time turned out to be a bit of a bubble, leading to few major changes.

But this time, he said, is different, because of new technologies like more-ubiquitous wireless internet, tablet computing, cheap video storage, and data in the cloud, and because of a tipping point in student demand. He envisions a future “five to ten years out” where school budgets for IT, textbooks and assessments will no longer be separated, creating a single K-12 funding pool of $9 billion a year for all kinds of new technology, both content and systems. “We’re just on that cusp, where the tablet and PC are rich enough and cheap enough that that’ll be the way it’s done.”

Equal with the flashy gadgets in the classroom, Gates highlighted the increasing use of back-end technologies to coordinate and manage data that can help drive districts, teachers and even students’ decisionmaking.

Amid all the enthusiasm, what’s still missing in the education space, Gates acknowledged, are “the gold standards of proving that something works.” In the areas of global health where the Gates Foundation has done so much work, you can measure outcomes by countless widely agreed-upon metrics: infant-mother life expectancy, number of malaria infections, number of vaccines distributed. In education, we have standardized test scores; graduation rates; maybe some surveys on job placement or unemployment rates. And we have some initial findings on what teaching techniques may be correlated with success for students. All these attempts at measurement are hotly contested, methodologically weak, several steps removed from the problem, or all three.

But as hundreds of spectators held up mobile phones to photograph Gates on stage, the sheer ubiquity of wireless technology in our daily lives seemed to provide its own internal rationale for its increasing use in the classroom–if for no other reason than that the classroom needs to resemble the wired, independent, collaborative, location-agnostic workplace that our kids will one day be joining.

Note: The Bill & Melinda Gates Foundation is among the various funders of The Hechinger Report.

The five most important ed-tech trends at SXSWedu


I’ve been on the ground in Austin for the South By Southwest Education Conference & Festival for 22 hours. In that time, I’ve interviewed six people, chatted with many more, and hit the Java Jive in the Hilton four times. Here’s what I see as the biggest trends coming out of the conference.

  1. Data and analytics. There seems to be a consensus, which Bill Gates will no doubt highlight in his keynote tomorrow, that the most important potential—as yet unrealized—contribution of technology to teaching and learning is the ability to extract meaningful insights from the myriad information that students generate as they travel through life on their learning journeys: diagnostics, individualized goals and plans, demographic information, performance evaluations, and on and on from cradle to mortarboard. Companies like InBloom and Engrade envision a teacher working like a doctor, synthesizing reams of test results and other information with the help of tech tools to arrive at the proper intervention for the proper moment.
  2. Games and adaptive learning. What makes video games fun is that they get harder as you get better at them, keeping you in the right “proximal zone” between bored and frustrated. “In the gaming world, when you don’t get the right outcome, you don’t feel like a failure, you say how do I adjust,” says Dreambox CEO Jessie Woolley-Wilson. This is what is meant, at its simplest, by adaptive learning. Game-like learning platforms range from Dreambox, a math program that “puts the learning in front,” in the words of Woolley-Wilson, to Kuato Studios, which later this month is debuting a fighting-robot coding game made by designers who worked on Call of Duty. Games and adaptive learning are intimately related to #1, data and analytics. In some sense, what defines a game is simply that the players are keeping score, so a key feature of online learning games is the constant generation of data that can, in theory, be used by teachers and parents in coaching mode to help direct students. Taken together, #1 and #2 form the megatrend/buzzword of “personalization”—the “mass customization” of learning.
  3. MOOCs. While many in the education space might be sick of hearing about Massively Open Online Courses, Coursera, edX, et al, they are still adding users and shaping the public imagination about what’s possible when classrooms open a window on the world.
  4. Makers and creativity. I was pleasantly surprised to see a Makerspace onsite at the convention center, where you could drop in and play with Legos, circuits and homemade play-doh. This hands-on, amateur, DIY stuff taps into a deep need for learners to accent what is most fully human, even as we are increasingly overwhelmed by virtual worlds. In addition, John Maeda, president of the Rhode Island School of Design, hosted an influential panel on STEM to STEAM—putting the arts into STEM education. He’s argued that the forward march of technology will lead to a higher premium being placed on the personal, well-designed and handmade.
  5. Going back to the classroom. “Where are the districts?” “Where are the teachers?” Aside from a few leaders of charter schools I’ve run into, most of whom were presenting, my impression is that there are few full-time educators here, let alone people who make IT purchasing decisions for school districts. Many sense a fundamental disconnect on both sides between the innovation conversation going on here and the real needs of teachers in classrooms. Hopefully that will change soon.

Big data and schools: Education nirvana or privacy nightmare?

InBloom, a nonprofit start-up founded with funding from the Bill & Melinda Gates Foundation and Carnegie Corporation, is taking center stage and spreading around some significant funds as an official sponsor of the South by Southwest Education conference in Austin, Texas this week. It hosted the official opening night party on Tuesday, is sponsoring a “networking lounge” with free coffee and snacks at the Hilton next to the convention center, and is debuting the first live demonstrations of its technology with representatives from pilot districts and states.


Iwan Streichenberger

It’s quite a splash for what is basically a highly technical, behind-the-scenes infrastructure company. InBloom promises to bring all the potential of “big data” to classrooms in a big way for the first time. Its stated mission: to “inform and involve each student and teacher with data and tools designed to personalize learning.”

“We want to make personalized learning available to every single kid in the U.S.,” says CEO Iwan Streichenberger. “The way you do this is by breaking the barriers—making data much more accessible.”

But to some educational activists, InBloom represents a danger, not an opportunity.

InBloom began as the Shared Learning Collaborative in 2011. It gets a bit technical, but basically, 10 districts in nine states agreed to build a shared technology infrastructure. Currently, student data—from attendance to standardized test scores—are locked in dozens of different “student information systems” that don’t talk to each other. “In one district we work with in Massachusetts, teachers had to use 20 different assessment storage places with different log-ins,” says Streichenberger.

InBloom offers a single middleware layer that hosts student data using Amazon Web Services, with some centralized dashboard-style functions and an API (application programming interface) that would allow start-ups to build education apps, aligned with Common Core standards, that anyone could use. It’s a similar strategy to how Facebook and Apple allow outside developers to build apps that pull your profile information from the cloud. Instead of designing for  thousands of school districts across the country, all of whom have their own idiosyncratic data storage systems, the InBloom platform will eventually allow developers to build one application—like DreamBox, a differentiated math game, or Kickboard, a dashboard program that allows teachers to track students’ performance and behavior—and have it work automatically in several states. This coordination, in turn, is likely to attract even more technology entrepreneurs to a market for educational IT spending estimated to be worth $20 billion in 2013. And similar to the way that electronic health records promise to reduce costs and increase efficiency and effectiveness in medicine, the use of centrally hosted data, says Streichenberger, offers similar cost savings and improvements in education.

But the very moves that make this idea a huge opportunity from the point of view of edtech entrepreneurs—the ability to find a large market for learning games and systems all in one place, to pull student data automatically, and to coordinate effortlessly with other apps—makes parents “horrified,” in the words of school activist Leonie Haimson of Class Size Matters.

“There are no limitations on the time-frame, or the kind of data. There’s no provision for parental consent or opt-out. The point is to give our kids’ data away for free, and share it as widely as possible with for-profit ventures to help them market and develop their learning products,” she says. “For-profit vendors are slavering right now at the prospect of being able to get their hands on this info. and market billions of dollars of worth of so-called solutions to our schools.”

Class Size Matters has been working with a lawyer to get public access to the agreements between InBloom and the nine states that are members of the collaborative (New York, Massachusetts, Louisiana, Colorado, Illinois, North Carolina, Georgia, Delaware and Kentucky), to learn under what circumstances student data will be released, and whether there are potential violations of FERPA, the Family Educational Rights and Privacy Act, which generally requires written consent from parents to release the records of students under 18. They are also trying to get states to agree to opt-out policies so parents can withhold children’s information from InBloom, especially sensitive information like disciplinary records, health records, and personally identifiable details like addresses.

Streichenberger says that InBloom’s terms of service are fully compliant with FERPA, but privacy policies—including parental notification and opt-out—will be in the hands of individual districts, which will hold and control all access to the data that InBloom hosts. “Privacy is a very emotional issue,” he says. “I have two children, four and six. I would never join InBloom if I thought it would compromise my kids.” At the same time, he says, “The privacy discussion is an important one, but one of my concerns is it’s preventing the discussion of what’s going on in the classroom. Are we preparing the children for the future? Do we have the tools to prepare them for the jobs of tomorrow?”

So far, Haimson says, her group has generated thousands of letters from parents concerned about their students’ privacy to the Gates Foundation and to individual states. She says that her biggest problem in spreading the word is that many parents don’t believe this is really happening. “Parents are outraged and can’t believe it’s legal,” she says. “The tech companies and foundations shrug their shoulders. People are living on two separate planets.”

Note: The Bill & Melinda Gates Foundation and Carnegie Corporation are among the various funders of The Hechinger Report.

Social media and video games in classrooms can yield valuable data for teachers

Photo by BarbaraLN

Social media, video games, blogs and wikis are playing increasingly important roles in classrooms across the country. Some worry that incorporating more social media and other technologies into education is leading to too much computer time, as well as to a generation of students deficient in the face-to-face social skills needed to survive in the workplace. Proponents say schools need to find ways to use these technologies to improve teaching and learning, or else risk losing the attention of digital natives.

A paper released earlier this week by the Brookings Institution addresses how social media, blogs and video games are improving education by increasing access to people and information in various forms, including Twitter feeds, blog posts, videos and books. These tools are also increasing people’s ability to share information with networks and contribute their own thoughts.

In a panel convened at Brookings yesterday to discuss how technologies like social media and video games are influencing education, the hot topics of “analytics” and understanding student data were discussed. Constance Steinkuehler Squire, a senior policy analyst in the White House’s Office of Science and Technology Policy and an expert on the educational uses of video games, said that beyond increasing student engagement, video games create valuable “data exhaust” by tracking each student’s progress.

“The data you can get from a student interacting with the game is compelling,” Squire said in the panel yesterday. “And it opens up an entire new area of formative assessment—and the mapping of formative assessments and learning analytics—to a game-type environment, which has been of a lot of interest, both private and public.”

The idea is that the data collected by video games and social media sites can be provided, sometimes in real time, to teachers who can then use it to better understand their students and tailor instruction to meet individual needs.

Janet Kolodner of the National Science Foundation said that data collection will come to be about more than that. She mentioned that NSF just launched a project on “big data”—a term that encompasses the gathering of extremely large amounts of data to which analytics are applied to reach new insights—and said that big data will play a much bigger role in education in the future.

“Big data is also being used so that if we have kids learning in the context of games or kids learning in the context of tutoring systems, that the system will be able to analyze that student’s work and their understanding and be able to give the right kind of feedback at the right time to help them deepen their understanding,” Kolodner said.

Companies like KnewtonJunyo and the Learn Lab in Pittsburgh and are all creating such systems that are being used by many schools across the nation.

Another concern of multiple audience members at the panel was the idea that advances in digital technologies would make school as we know it irrelevant. They suggested a day might come when students wouldn’t go to a school building at all, but would instead learn exclusively from mobile devices and virtual teachers. Kolodner put their fears to rest.

“I don’t think schools are going to go away,” she said. “Parents need to work, and you need a place to put the kids.”

A wave of laughter spread across the room.