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Thursday, October 20, 2016

CA finds that Google Complies with FERPA and CA Privacy Requirements

CETPA finds Google Education AppsComplies with AB 1584 and FERPA
Hundreds of school districts throughout California and the rest of the country use a suite of education products formerly called Google Applications for Education (“GAFE”) and recently renamed G Suite for Education ("G Suite").  Questions have been raised as to whether G Suite complies with the Family Educational Rights and Privacy Act (“FERPA”) found in 20 U.S.C. 1232(g), and California Assembly Bill (“AB”) 1584 found in California Education Code section 49073.1.  Both laws establish privacy protections for pupil records stored or analyzed by digital education providers.  These protections include, but are not limited to:

(1) prohibitions against the unauthorized use of the records;
(2) requirements to allow a student or parent to access the records;
(3) disposal of the records at the end of the term of the agreement; and
(4) requirements to maintain adequate safeguards for the records, including notification of unauthorized access to pupil records.

One reason questions may have been raised about G Suite data privacy compliance is that not all FERPA and AB 1584 required terms are found in the G Suite for Education Online Agreement (“G-Suite Online Agreement”).  Given the widespread use of G Suite there was an urgent need to determine whether G Suite complies with FERPA and AB 1584.  CETPA and its counsel, Fagen Friedman & Fulfrost, undertook an extensive analysis of the G Suite privacy terms and conditions, including the G Suite Online Agreement.

The analysis found that central to determining privacy compliance is understanding that the G Suite privacy terms and conditions consist of more than just the G Suite Online Agreement.  FERPA and AB 1584 requirements can be found in several additional G Suite documents, including but not limited to: the G Suite Privacy Notice; the Additional Terms for Use of Additional Services; and the Data Processing Amendment to G Suite Agreement.

When taken together, these documents show that G Suite likely complies with all material portions of FERPA and AB 1584.  In most cases, the elements of the data privacy requirements are explicit in G Suite's privacy terms and conditions.  However, some aspects of FERPA and AB 1584 requirements are not referenced verbatim in privacy terms and conditions.  Some of those requirements require statutory interpretation to conclude that G Suite has met a particular data privacy requirement

There are two factors of the data privacy compliance laws that require statutory interpretation to conclude that G Suite has met its obligations under FERPA and AB 1584.  The first requirement is to provide students with the opportunity to establish a separate account for student-generated work and the second is the right of a parent to access a pupil's record and correct any erroneous information.  Currently, the G Suite privacy terms and conditions state that G Suite will only take a ministerial role to meet these obligations.

The issue then is whether the federal and state laws require Google to actively manage these processes, or whether Google’s role may be ministerial by implementing the directions of a school district.  We conclude that the creation of a separate account and the modification of pupil records is a power that AB 1584 and FERPA intended to leave to school districts and not to Google.  Therefore, Google’s ministerial role stated in the G Suite privacy terms and language on these two topics is consistent with the intent of the statutes.

CETPA’s finding that the G Suite data privacy terms and conditions comply with FERPA and AB 1584 is bolstered by recent findings of Ernst & Young, which held that G Suite privacy terms and conditions were consistent with the privacy standards established by the International Standards Organization for data privacy.  These standards have many similarities to those found in FERPA and AB 1584.

We hope this guidance will provide reassurance to the many California school districts using or contemplating the use of G Suite.  Please note that student data privacy laws are relatively new and subject to interpretation, and G Suite may amend its terms and conditions at any time, which could alter the foregoing analysis and findings.  CETPA has posted the underlying legal analysis supporting this conclusion on - The F3LAW Partner Resources Page

Andrea F. Bennett
Executive Director, CETPA

Monday, October 10, 2016

Jeff Sacks - Smart machines and the future of jobs

Smart machines
and the future of jobs
By Jeffrey D. Sachs

Since the early 1800s, several waves of technological change have transformed how we work and live. Each new technological marvel — the steam engine, railroad, ocean steamship, telegraph, harvester, automobile, radio, airplane, TV, computer, satellite, mobile phone, and now the Internet — has changed our home lives, communities, workplaces, schools, and leisure time. For two centuries we’ve asked whether ever-more-powerful machines would free us from drudgery or would instead enslave us.

 The question is becoming urgent. IBM’s Deep Blue and other chess-playing computers now routinely beat the world’s chess champions. Google’s DeepMind defeated the European Go champion late last year. IBM’s Watson has gone from becoming the world’s “Jeopardy’’ champion to becoming an expert medical diagnostician. Self-driving cars on the streets of Pittsburgh are on the verge of displacing Uber drivers. And Baxter, the industrial robot, is carrying out an expanding range of assembly-line and warehouse operations. Will the coming generations of smart machines deliver us leisure and well-being or joblessness and falling wages?

 The answer to this question is not simple. There is neither a consensus nor deep understanding of the future of jobs in an economy increasingly built on smart machines. The machines have gotten much smarter so fast that their implications for the future of work, home life, schooling, and leisure are a matter of open speculation.

 We need to pursue policies so that the coming generation of smart machines works for us, and our well-being, rather than humanity working for the machines and the few who control their operating systems.

 In a way, the economic effects of smarter machines are akin to the economic effects of international trade. Trade expands the nation’s economic pie but also changes how the pie is divided. Smart machines do the same. In the past, smarter machines have expanded the economic pie and shifted jobs and earnings away from low-skilled workers to high-skilled workers. In the future, robots and artificial intelligence are likely to shift national income from all types of workers toward capitalists and from the young to the old.

 CONSIDER ENGLAND’S Industrial Revolution in the first part of the 19th century, when James Watt’s steam engine, the mechanization of textile production, and the railroad created the first industrial society. No doubt the economic pie expanded remarkably. England’s national income roughly doubled from 1820 to 1860. Yet traditional weavers were thrown out of their jobs; the Luddites, an early movement of English workers, tried to smash the machines that were impoverishing them; and poet William Blake wrote of the “dark Satanic mills’’ of the new industrial society. An enlarging economic pie, yes; a new prosperity shared by all, decidedly not.

 Looking back at two centuries of more and more powerful machines (and the accompanying technologies and systems to operate them), we can see one overarching truth: Technological advances made the society much richer but also continually reshuffled the winners and losers. Similarly, one overarching pattern was repeatedly replayed. The march of technology has favored those with more education and training. Smart machines require well-trained specialists to operate them. An expanded economic pie favors those with managerial and professional skills who can navigate the complexities of finance, administration, management, and technological systems.

 Overall, better machines caused national income to soar and the man-hours spent in hard physical labor to decline markedly. Seventy-hour workweeks in 1870 have become 35-hour workweeks today. An average of around six years of schooling has become an average of 17 years. With increasing longevity, most workers can now look forward to a decade or more of retirement years, an idea simply unimaginable in the late 19th century. It’s amazing to reflect that for Americans 15 years and over, the average time at work each day is now just 3 hours 11 minutes. Those at work average 7 hours and 34 minutes, but only 42.1 percent of Americans 15 and over are at work on an average day. The rest of the time, other than sleep and personal care, is taken up with schooling, retirement, caring for children, leisure and sports, shopping, and household activities.

 Smart machines in the 19th century provided massive power (the steam engine), transport (rail, steamships, automobiles), information (telegraph), and material transformation (steel and textile mills), and also, crucially, a more and more powerful substitute for human brawn – that is, backbreaking physical labor — on the farm and in the mines. Seed drills, cotton gins, threshers, reapers, combined harvesters, and by the early 20th century, tractors, not only opened up vast new farmlands but also replaced millions of farm workers by machines. Mechanical cotton pickers in the early decades of the 20th century displaced millions of African-American sharecroppers on Southern farms and contributed to the great African-American migration to northern cities.

 Hard physical labor declined as machines did more and more of this work; but so too did jobs and earnings for lower-skilled workers. Those lucky to get an education could obtain the higher skills needed for the new jobs. Those who could not suffered stagnant or falling wages and a further loss of social status. In the past two decades, more and more low-skilled men have simply dropped out of the labor force entirely.

 The most important policy response is to ensure that students stay in school long enough to achieve the skills they need for the new and better jobs. As long as the national supply of skilled workers roughly keeps pace with the rising demand for skilled workers, while the supply of low-skilled workers declines in line with the decline in the numbers of low-skilled jobs, the gap in earnings between high- and low-skilled workers remains relatively stable. In this way, the rising school attainments of Americans during the 20th century roughly maintained a balance with the shift from low-skilled work to high-skilled work.

 Yet after around 1980, the earnings of highly educated workers (notably, those with bachelor’s degrees and higher) increased sharply relative to less-educated workers (those with high-school diplomas or less). Greater international trade and offshoring probably had a role in this, and so did technology, with smarter machines replacing high-school-educated workers in a widening range of manual and repetitive tasks. The shift of the labor force toward higher-skilled workers wasn’t fast enough in recent decades. Many American lower-skilled workers have been hit hard by lost jobs and falling wages.

 YET TODAY’S smart machines now are not just replacing brawn but also brains. The futurist Ray Kurzweil and others have popularized the term “singularity’’ to mean a time in the near future when machines are simply better than humans at just about everything: moving, assembling, driving, writing, calculating, war-making, teaching (yikes!), and the rest.

 Several recent studies, including at Oxford University and McKinsey, have tried to estimate the share of jobs that are likely to be up for grabs by smart machines in the next 20 or so years. Each occupation is analyzed for the kinds of tasks needed. Are they highly repetitive or highly context-specific? Do they require highly specialized mechanical skills, a high degree of interaction with others, or a high measure of emotional empathy? And so on. From this categorization of job tasks, the researchers estimate the share of jobs that can be substituted by robots and artificial intelligence systems. Their answer: Roughly half of today’s jobs are susceptible to at least some kinds of replacement by smart machines.

 The implications are a bit tricky. On the one hand, smarter machines mean more economic output and, in principle, a larger economic pie to share among the American people. Investing in machines, or in the companies that produce the smart systems that run them, would seem to offer high returns; capital owners would be very likely to benefit. On the other hand, smarter machines could mean a decline in the demand for workers. Young people with labor to sell but little wealth to invest could find themselves on the short end of the economic stick, with lower wages and no grand prospect of benefiting from the higher returns to capital. Older and richer Americans would tend to benefit, younger and poorer Americans would tend to fall behind.

 This would not be the end of the story, however. If today’s young people find themselves without jobs, they not only will be poorer, but will also save less as a result of shrunken incomes. Yes, the smarter machines will offer a higher return to saving, but the supply of national saving will shrink. A careful theoretical analysis reveals a stark truth: Smart machines could actually set in motion a downward spiral, wherein today’s young workers can’t find decent jobs, and thereby cut back on their saving, which in turn leaves the following generation of young workers even worse off.

 This is indeed a frightening vision. And yet the same analysis suggests a way out. If the rich capital owners transfer some of their windfall profits to the struggling young workers, then both the old rich and the young poor would be better off with the smart machines than without them. In effect, the rich older shareholders would compensate the poor younger workers in order to offset the fall in wages.

 There are two ways this “offset’’ could happen. Within families, parents could transfer some of their increased wealth to their children; but alas, that is a solution that is likely to be relevant mainly for richer households.

 For the non-rich, the real solution could and should be achieved through fiscal policy. Rich older shareholders should be taxed in order to make transfer payments to the poorer, young workers.

 Such transfer payments could be carried out in many ways: a cut in payroll taxes; tuition-free higher education; an expansion of the Earned Income Tax Credit (EITC) for low-wage workers; or a “reverse’’ Social Security system with payments from the old to the young. One policy that has been suggested is a capital grant to every newborn, financed by a wealth tax. In essence, each newborn would receive a robot (or financial claim to one) at birth.

 THE NEW AGE of smart machines has already seen a shift in national income away from wages and toward profits. In automobile manufacturing, for example, where robots have already displaced many assembly-line workers, the share of wage compensation in the industry’s value-added has tumbled from 57 percent in 1997 to 47 percent in 2014. For the economy as a whole, a recent study reports a decline in the labor share of national income from around 68 percent in 1947 to 60 percent in 2013. The shift toward capital income seems to be well underway, and would seem to be a key factor in America’s sharply higher inequality of income. As machines become even smarter in future years, the economy-wide shift from wage income to profit income is likely to continue.

 In addition to income redistribution from capital owners to workers (and from old to young) there are three other steps we should plan to take.

 First, as old jobs disappear and new ones are created, we should emulate Germany’s successful apprenticeship programs, which train young workers in the skills needed in the economy. The President’s Council of Economic Advisers has rightly emphasized the need for scaling up this kind of active training.

 Second, we should prepare for a workforce in which workers will change jobs with much greater frequency than in the past. In an age of disruptive technology, we should plan for disruption. Changing jobs should be regarded as normal; training and skill upgrading should be life long, and health care and other benefits should follow workers, not jobs.

 Third, and finally, let us remember that ever-smarter machines could enable us to enjoy much more leisure time, and more hours of the day at valuable but nonremunerated activities and volunteer work.

 Suppose that singularity indeed arrives, so that robots and expert systems really do perform all the unpleasant and humdrum work of the economy. As long as fiscal policies ensure that everybody, young and old, can share in the bounty, the results could be a 21st-century society in which we have much more time — and take more time — to learn, study, create, innovate, and enjoy and protect nature and each other.

 Jeffrey D. Sachs is University Professor and Director of the Center for Sustainable Development at Columbia University, and author of “The Age of Sustainable Development.’’  

Tuesday, October 4, 2016

What "jobs" do educators need help t o solve?

In his new book, Against Luck: The Story of Innovation and Customer Choice, Clay Chistensen argues: "Successful innovation can seem like a matter of luck—but it need not be so. Every day, “jobs” arise in people’s lives that they need to resolve. Needing to get one of these jobs done is the mechanism that causes people to “hire” an offering—whether it be a product or a service.."

So what are the jobs in education that school districts want to "hire" services to help resolve:

  1. Manage Competencies.  An individual educator or an education entity can create a competency framework that aligns to other source standards documents like the Common Core, TEKS, and Next Generation Science Standards to be used by assessment, content and curriculum management, and student learning record tools.

  1. Manage Digital Content and Curriculum.  An individual educator or an education entity can curate a collection of content organized in a logical progression and tagged to learning standards that can be used (via LTI or TCC) by one or more learning management system to support standards-aligned instruction.
  1. Deliver Classroom Competency Assessments.  An educator can create and administer a classroom assessment that produces standards-aligned outcome and proficiency level that can be integrated with other assessment results (though xAPI/Caliper).

  1. Deliver Interim Benchmark Assessment.  A district assessment office can curate and support classroom administration of common interim benchmark assessments that can be integrated with other assessment results (though xAPI/Caliper).

  1. Integrate Embedded Assessment Learning Tools.  An educator, parent, or learner can use an assessment embedded learning tools (e.g. Kahn, IXL, 10 Marks, Accelerated Reader) to supplement and support learning and produce diagnostic assessment data that can be integrated with other assessment results (though xAPI/Caliper).

  1. Sustain Access to an Integrated Student Learning Record.  A user can access a view of a student’s learning profile that integrates (though xAPI/Caliper) standards-aligned assessment results (outcomes) from multiple sources including assessment embedded learning tools, interim benchmark assessments, and classroom assessments scored to standard rubrics.

  1. Support Personalized Learning.  An educator, learner, parent, or extended learning agent authorized by the parent can access an integrated learning profile that summarizes from multiple assessments instruments the competencies a learner has demonstrated a level of performance and provides access to instructional materials and interventions that are customized to that learner’s profile.

Thursday, September 29, 2016

Alex Hernandez

Personalizing with technology isn’t easy—especially when the user has to adjust to the product, rather than the other way around.

Take KIPP Bay Area math teacher Tricia Dong. A few years ago, she could see that some of her fifth graders struggled with second grade math skills, while others were capable of eighth grade content on the NWEA Measures of Academic Progress (MAP) assessment. She wanted to create unique content for each of them—but it wasn’t easy. “The first time I created Khan Academyplaylists for each of my 100 fifth graders,” says Dong, “it took me 6 hours.”

Individualizing lessons for students is a still a manual process for most teachers.

Individualizing lessons for students is a still a manual process for most teachers—Dong’s experience is not unusual—which means customization can happen sporadically or not at all. But for good learning to happen, customization is a non-negotiable.

Four KIPP teachers decided to take matters into their own hands, responding to this frustration by building something better. Today, KIPP Bay Area teachers can generate personalized Khan Academy playlists for their students in under 10 minutes using an automated tool. The tool was designed by KIPP Bay Area teachers and, unsurprisingly, solves a real problem while saving time.

How’d they do it? Here’s their story.

Building Tools That Teachers Want To Use

KIPP Bay Area is a public charter school network operating 11 schools serving 4,700 K-12 students. Seventy-seven percent of the network's students are low-income, and 95% are students of color. And like in many districts and schools, teachers are surrounded by “helpful” tools that are not very helpful.

For example, KIPP students are assessed three times a year on the NWEA MAP test. MAP is unique because it measures students’ academic growth throughout the year. It can tell a teacher that their 7th grade student started the year with the math skills of a typical 4th grader and ended the year performing like the average 6th grader. Traditional assessments do not capture that type of student growth data nor do end-of-year state tests. But there’s a problem: the data is not actionable for teachers. What does a teacher do now that they know Carlos has a 174 scale score in “Measurement and Data”?

"Data that is not actionable plus online content that is not customized is a recipe for disappointment." Jennie Dougherty, KIPP Bay Area Associate Director of Innovation

Prior to the 2014-2015 school year, teachers also had access to Khan Academy, where math content is organized by grade level. But organizing content by grade level is not helpful when students are all over place in their knowledge and skills. “Data that is not actionable plus online content that is not customized is a recipe for disappointment,” laments Jennie Dougherty, KIPP Bay Area’s Associate Director of Innovation.

Teachers, by default, began creating their own solutions. In fall 2014, Khan Academy quietly released a PDF document that told teachers which Khan Academy exercises are most relevant to students based on their MAP scores. Kamal Pannu, who was a 5th grade math teacher back then, began manually assigning Khan Academy exercises to students using this guidance. “I prototyped an early version of a tool that created Khan Academy playlists based on students’ individual MAP scores, but the initial setup took me weeks, and it wasn’t very practical,” said Pannu.

Dougherty, who manages innovation for the region, noticed that Pannu and other teachers were making different hacks to the same problem. She worked with them to build an automated tool. The resulting 1.0 version was a Google spreadsheet (shown below) that allows teachers to copy-and-paste their students’ MAP scores and generate an individualized Khan Academy playlist for each student. 

All of a sudden, the MAP assessment data was actionable and the Khan Academy content was personalized. KIPP teachers understood that, in order for Khan Academy to be more useful in the classroom, students couldn’t just roam free in the software; the classroom experience needed coherence. The tool (pictured above) is a Rosetta Stone of sorts that takes a student’s individual MAP data, uses this data to help students move through Khan Academy in targeted ways—moving up or down grade levels as appropriate—and allows teachers to link the work on Khan Academy to the classroom.

Let’s dig into how the playlists are used.

These Teachers Have Moves Like Jagger

The customized Khan Academy playlists have completely transformed KIPP’s classrooms! Ok, just kidding. But in all seriousness, the playlists became an important part of these teachers’ classrooms, helping struggling students close skill gaps and advanced students access new content.

During the start of the 2015-2016 school year, KIPP Bay Area began implementing the Eureka Math curriculum while adopting the Common Core standards. In Ms. Dong’s classes, she grouped her 5th graders into low, medium and high groups, and each group did three rotations during a 70 minute period—using a combination of Khan Academy and Eureka Math. See below.
Rotation 1
Rotation 2
Rotation 3
Small group Eureka lesson with teacher
Independent practice on Eureka
Khan Academy exercises to fill skill gaps
Students try Eureka lesson on their own
Small group Eureka lesson with teacher
Independent practice on Eureka
Students try Eureka lesson on their own
Khan Academy extension exercises
Small group instruction with teacher to correct Eureka misperceptions or support extension activities

The structure allows Dong to spend her time pushing students’ conceptual understanding on Eureka math while giving students customized practice opportunities on Khan Academy.

Four teachers—Neil Davis, Tricia Dong, Katie Hogan, and Kamal Pannu—stood out among their colleagues as having the best methods to combine small group instruction, individual support and customized practice opportunities for students.Their students’ growth on the MAP test that year ranged from promising to earth-shattering. Hogan and Pannu, for example, finished the year with two-thirds of their students scoring in the top quartile nationally.

This success led KIPP Bay Area to refine the tool and release it to all math teachers in the fall of 2015. And here’s a crazy stat: Dougherty’s team generated individualized playlists for over 3,600 students during September alone.

A little alignment created hunger for more—and these teachers aren’t stopping the design process anytime soon.

Hungry for More Ultimately, these KIPP teachers wanted tighter alignment between their classroom lessons and what students were doing online. They appreciated online learning’s potential to provide personalized practice, but it needed to be part of coherent classroom experience.

Aligning MAP to Khan Academy was a first step. But a little alignment created hunger for more—and these teachers aren’t stopping the design process anytime soon.

“Let’s break the mystery of ‘black box’ online content and align Eureka Math to Khan Academy,” says Pannu. “For each Eureka lesson, let’s find the foundational math skills on Khan Academy for students who are 1-2 years behind grade level.”

Sounds like another tool to build. Take your pencils and notebooks out, Silicon Valley. Class is in session.

Alex Hernandez (@thinkschools) is a partner at Charter School Growth Fund, a nonprofit that supports the growth of the nation's best public charter schools. Charter School Growth Fund provides philanthropic support to KIPP Bay Area. Alex is also an official EdSurge columnist.  

Friday, August 19, 2016

Project Management 101, 2.0

To paraphrase my brother Ted:

  • "Nothing exists unless it's written down.  Oral communication is the enemy.  That is the essence of project management.  Everything else is a detail."

Good project management comes from an innate discipline to intermediate between the real-time world and the planned world by writing things down.   The exact format, process, and tools used should be appropriate to the project complexity and size.

There are, however, some essentials which I use use every time.  Here is my checklist for each project I am involved with:

  • Step 1, set up a shareable folder for working documents.  I use Google Drive.  The support for real-time collaboration is unparalleled. 
    • Step 2, create a spreadsheet.  Again, I use Google Sheets now.  I used to always use (and love) Excel. 
      • Step 3, create a tab for People and for Tasks.  
      • Step 4 (for any formal project), create a project charter and sign contracts (where appropriate).  A contract is an agreement between organizations, typically, although it can apply to individuals.  A charter is typically between the people involved with the project.  A charter can be used by both formal and informal projects to write down the key information needed to start a project successfully.

      • Step 5 is where project complexity begins to create real variance in approach.  For a simple project, a list of tasks and people may be all that is needed.  Often additional lists in the workbook can be generated for the specifics of the project.  For more technical projects, a threaded discussion with workflow like JIRA and project schedule and resource tool like MS Project are often used.  

      Putting Private Capital to Work for Public Innovation

      The Education Department said today it will double down on the Pay for Success model to fund early learning with a $2.8 million grant competition open to states and local governments wishing to study the feasibility of the model in their area.

      The Pay for Success model is centered around social impact bonds, where a government agency or contractor pays a service provider for the achievement of pre-established outcomes. Typically, private entities absorb the upfront costs and are reimbursed later.

      Among the measurable outcomes that Education Department officials are suggesting: kindergarten readiness, improved social and emotional skills, improved executive functioning, reduction in grade retention and in the need for later special education, reduction in discipline referrals and interactions with law enforcement, and increases in high school graduation.

      The grant will fund seven to 14 entities, with awards ranging from $200,000 to $400,000. The dollars are not meant to pay for the implementation of Pay for Success programs, just the development of local feasibility studies.

      Education Department officials have praised the results of a PFS program in Utah, where the model has gained the backing of state lawmakers. Backed by Goldman Sachs, it has also garnered criticism for overstated success.

      House lawmakers passed a bill to fund social impact bonds in June. 

      Thursday, July 14, 2016

      Rich Miner: Google's New Education Leader

      Google is starting a new project arm that specifically focuses on education, supposedly expected to produce more quality apps built for a school setting.

      While there are already plenty of educational apps for kids from the Alphabet-owned company, like Cast for Education, Google Classroom and the recently updated Google Cardboard, which now offers affordable virtual reality (VR) programs for teachers, Android co-founder Rich Miner still finds this department lacking.

      "[There is a] difficulty in finding quality educational apps and other kid-focused Internet services that weren't primarily either: (a) Babysitters; and/or (b) Ad delivery devices," reveals Fortune writer Dan Primack when he previously found himself in a seemingly random elevator exchange with Miner, with topics mostly centered around their kids.

      That said, the publication revealed that Miner was stepping down from his role as one of Google Ventures' (GV) general partner and moving forward, would become Google's education venture partner instead, though, retaining seats across his existing GV posts. This should give him more time to fully realize his plans for the project, something he's been reportedly working on for the past two years, while still working closely with the Alphabet group.

      "[W]hat was to my benefit years ago is now to my detriment," comments Bill Maris, CEO of Google Ventures, who was not surprised that his former staff member was leaving the venture branch in pursuit of "build something new." The CEO added that Rich Miner is "an adventurous person who jumped into this thing with [him]" and he is "really excited" to see what Miner has in store for the new project.

      Although Miner had the opportunity to start up a new company under the Alphabet brand, it only made sense for the former general partner to execute his plans as a Google project due to the company's existing line of educational resources mentioned above. It is unknown at this point, however, if the new "education-focused" project spearheaded by Miner will consider (and absorb) the current Google for Education programs in its future plans.

      "I don't actually know exactly what we're building yet," Miner described, explaining that his vision "is another big idea," in reference to Android and WildFire's long development over the years, and as such, his education project under Google "will take time."

      The new initiative will house its own dedicated team in the main Google HQ back at Mountain View, though, Miner plans to work from his place in Cambridge, Massachusetts, where most of current GV investors are based.

      © 2016 Tech Times, All rights reserved. Do not reproduce without permission.

      - See more at: http://www.techtimes.com/articles/168998/20160708/android-co-founder-rich-miner-starting-google-education-project.htm#sthash.Ty1CM4cS.dpuf

      Google slammed over its 'free' school service

      Google slammed over its 'free' school service

      Sweden sour – source

      Two Swedish researchers have torn into Google's free school service, accusing the online giant of purposefully misleading users in order to continue profiting from the sale of children's data.
      Maria Lindh and Jan Nolin from the University of BorĂ¥s have published a research paper [note: paid access] that digs into the policies around the Google Apps for Education (GAFE) service, and concluded that it has gone to some lengths to "conceal" the business model from school administrators.
      By using a range of rhetorical devices and misleading definitions, the company is able to advertise that GAFE will not include any ads and "your data is yours," while at the same time selling the information it accumulates to advertisers for significant profit, they conclude.
      "Educators and pupils become reconfigured as targets for data mining, tracking and measurement," the paper argues.
      One of the key distinctions comes in Google's own fluid definitions of "data" and "information," which it uses to make bold claims while actually doing what many perceive is the opposite of what they have understood. The other key term is "processing."
      "Google processes your data to fulfill our contractual obligation to deliver our services," it states, adding: "Google's customers own their data, not Google."
      It goes on: "The data that companies, schools and students put into our systems is theirs. Google does not sell your data to third parties."

      And then

      Which all sounds very convincing until you start reading Google's "Data Processing Amendment" and other official documents relating to the GAFE service. Lindh and Nolin state they were struck by how the company carefully phrased them to hide true intent.
      They note that the company is under a legal obligation to say that it commercially exploits its "constantly expanding archive" – ie, all the information it accumulates from people using the service – but that it goes to some lengths to hide the reality of the situation.
      "Several segments of the policy texts state clearly, or so it seems, that the archive is not exploited, ie, we do not sell your data," says the paper. But thanks to how Google chooses to define "data," that is not true. "Central terms, such as processing user data or content, are not explained," they note. And important information is hidden or left out.
      The sale of data is frequently framed as something that is done to improve services or user experience, and its commercial value is not touched upon.
      By contrast, the real business model is "belittled or framed as a minor aspect of what Google does." And when the company provides "examples" of what its policies mean in reality, it provides ones that don't cover the less savory aspects of what actually happens. "Google's systematic approach of giving one example connected to each claim is therefore misleading in more than one way," the paper notes.
      They conclude: "It is obvious that Google talks in different voices, deliberately wanting to make their business model less obvious for users, who mostly do not look deeply into Google's policies in any case."


      The authors paint a distinction between Google's back-end and front-end models: where the front end is free – services for schools – and the back-end is where it makes its money: "information packaged for profit."
      GAFE itself is a cloud-based service that bundles several Google services, including Google Classroom, Gmail, Google Drive/Calendar/Docs/Presentation/Sites and others, and provides them for free to schools.
      Google says it has 50 million students, teachers and administrators using GAFE, and price is a major reason why.
      "We came up with a suggestion that instantaneously would lower costs by one million," one former CEO of a Swedish education establishment told the researchers, despite having reservations about its legality, security, sales of data and tracking by Google. "Yes, but then what? The next item on the agenda! These are decisions that make themselves."
      But, the authors argue, Google is able to say it does not sell its users' "data" – but it is not the day-to-day data it profits from, but rather the longer-term "monitoring of behavior" where it creates "algorithmic identities of individual users" by combining collected and personal information.
      Those "algorithmic identities" then become of enormous value and are used commercially but not "sold" to third parties – allowing for the seemingly clear language about what happens to students' data.
      With the soothing language about Google not selling the information, the teachers and administrators that the authors talked to were more than happy to take on the benefits that comes from Google's services.
      Not only is the service free, but it is well-managed and run (no more constantly dealing with a small and impossible IT department), and brings novel advantages.
      Several teachers noted, for example, that they were able to watch the progress of their students' work. This not only helped clamp down on bad practices – such as the pasting of an entire paragraph (plagiarism?) or poor planning (three weeks to do the assignment and the document is created the night before) – but it also helps teachers see their students' thought processes and so both better understand and assist them in their learning, and save the teachers from the dreaded arrival of a stack of finished essays on the final day.


      The question, though, is whether these advantages outweigh the fact that one company is able to build up commercially valuable information on students, which Google can most likely continue over when they stop using the school system because of their multiple tie-ins with other Google services.
      Where are the regulators in this case? Trying their best. The Swedish Data Inspection Board brought an injunction against the first municipality to implement GAFE, Salem, in 2011 arguing that its pupils' privacy was under threat by the users of Google software.
      Four years later, and the "shortcomings in the agreement with Google" had finally been resolved. In March last year it said: "We can confirm that the flaws in the agreement that we have pointed out before have now been remedied."
      But, it also noted that it was now reviewing the agreement to see if there were any more issues. Critically, it noted that it "has not yet examined the processing of sensitive personal data in a cloud service."
      In other words, the topic of this entire paper in which it concluded Google was purposefully misleading people over what it was actually doing with their personal data. ®