Big tech may look troubled, but it’s just getting started — from nytimes.com by David Streitfeld

Excerpt:

SAN JOSE, Calif. — Silicon Valley ended 2018 somewhere it had never been: embattled.

Lawmakers across the political spectrum say Big Tech, for so long the exalted embodiment of American genius, has too much power. Once seen as a force for making our lives better and our brains smarter, tech is now accused of inflaming, radicalizing, dumbing down and squeezing the masses. Tech company stocks have been pummeled from their highs. Regulation looms. Even tech executives are calling for it.

The expansion underlines the dizzying truth of Big Tech: It is barely getting started.

 

“For all intents and purposes, we’re only 35 years into a 75- or 80-year process of moving from analog to digital,” said Tim Bajarin, a longtime tech consultant to companies including Apple, IBM and Microsoft. “The image of Silicon Valley as Nirvana has certainly taken a hit, but the reality is that we the consumers are constantly voting for them.”

 

Big Tech needs to be regulated, many are beginning to argue, and yet there are worries about giving that power to the government.

Which leaves regulation up to the companies themselves, always a dubious proposition.

 

 

 

The world is changing. Here’s how companies must adapt. — from weforum.org by Joe Kaeser, President and Chief Executive Officer, Siemens AG

Excerpts (emphasis DSC):

Although we have only seen the beginning, one thing is already clear: the Fourth Industrial Revolution is the greatest transformation human civilization has ever known. As far-reaching as the previous industrial revolutions were, they never set free such enormous transformative power.

The Fourth Industrial Revolution is transforming practically every human activity...its scope, speed and reach are unprecedented.

Enormous power (Insert from DSC: What I was trying to get at here) entails enormous risk. Yes, the stakes are high. 

 

“And make no mistake about it: we are now writing the code that will shape our collective future.” CEO of Siemens AG

 

 

Contrary to Milton Friedman’s maxim, the business of business should not just be business. Shareholder value alone should not be the yardstick. Instead, we should make stakeholder value, or better yet, social value, the benchmark for a company’s performance.

Today, stakeholders…rightfully expect companies to assume greater social responsibility, for example, by protecting the climate, fighting for social justice, aiding refugees, and training and educating workers. The business of business should be to create value for society.

This seamless integration of the virtual and the physical worlds in so-called cyber-physical systems – that is the giant leap we see today. It eclipses everything that has happened in industry so far. As in previous industrial revolutions but on a much larger scale, the Fourth Industrial Revolution will eliminate millions of jobs and create millions of new jobs.

 

“…because the Fourth Industrial Revolution runs on knowledge, we need a concurrent revolution in training and education.

If the workforce doesn’t keep up with advances in knowledge throughout their lives, how will the millions of new jobs be filled?” 

Joe Kaeser, President and Chief Executive Officer, Siemens AG

 

 


From DSC:
At least three critically important things jump out at me here:

  1. We are quickly approaching a time when people will need to be able to reinvent themselves quickly and cost-effectively, especially those with families and who are working in their (still existing) jobs. (Or have we already entered this period of time…?)
  2. There is a need to help people identify which jobs are safe to reinvent themselves to — at least for the next 5-10 years.
  3. Citizens across the globe — and their relevant legislatures, governments, and law schools — need to help close the gap between emerging technologies and whether those technologies should even be rolled out, and if so, how and with which features.

 


 

What freedoms and rights should individuals have in the digital age?

Joe Kaeser, President and Chief Executive Officer, Siemens AG

 

 

5 influencers predict AI’s impact on business in 2019 — from martechadvisor.com by Christine Crandell

Excerpt:

With Artificial Intelligence (AI) already proving its worth to adopters, it’s not surprising that an increasing number of companies will implement and leverage AI in 2019. Now, it’s no longer a question of whether AI will take off. Instead, it’s a question of which companies will keep up. Here are five predictions from five influencers on the impact AI will have on businesses in 2019, writes Christine Crandell, President, New Business Strategies.

 

 

Should we be worried about computerized facial recognition? — from newyorker.com by David Owen
The technology could revolutionize policing, medicine, even agriculture—but its applications can easily be weaponized.

 

Facial-recognition technology is advancing faster than the people who worry about it have been able to think of ways to manage it. Indeed, in any number of fields the gap between what scientists are up to and what nonscientists understand about it is almost certainly greater now than it has been at any time since the Manhattan Project. 

 

From DSC:
This is why law schools, legislatures, and the federal government need to become much more responsive to emerging technologies. The pace of technological change has changed. But have other important institutions of our society adapted to this new pace of change?

 

 

Andrew Ng sees an eternal springtime for AI — from zdnet.com by Tiernan Ray
Former Google Brain leader and Baidu chief scientist Andrew Ng lays out the steps companies should take to succeed with artificial intelligence, and explains why there’s unlikely to be another “AI winter” like in times past.

 

 

Google Lens now recognizes over 1 billion products — from venturebeat.com by Kyle Wiggers with thanks to Marie Conway for her tweet on this

Excerpt:

Google Lens, Google’s AI-powered analysis tool, can now recognize over 1 billion products from Google’s retail and price comparison portal, Google Shopping. That’s four times the number of objects Lens covered in October 2017, when it made its debut.

Aparna Chennapragada, vice president of Google Lens and augmented reality at Google, revealed the tidbit in a retrospective blog post about Google Lens’ milestones.

 

Amazon Customer Receives 1,700 Audio Files Of A Stranger Who Used Alexa — from npr.org by Sasha Ingber

Excerpt:

When an Amazon customer in Germany contacted the company to review his archived data, he wasn’t expecting to receive recordings of a stranger speaking in the privacy of a home.

The man requested to review his data in August under a European Union data protection law, according to a German trade magazine called c’t. Amazon sent him a download link to tracked searches on the website — and 1,700 audio recordings by Alexa that were generated by another person.

“I was very surprised about that because I don’t use Amazon Alexa, let alone have an Alexa-enabled device,” the customer, who was not named, told the magazine. “So I randomly listened to some of these audio files and could not recognize any of the voices.”

 

 

The Top 20 Education Next Articles of 2018 — from educationnext.org

Excerpt:

Every December, Education Next releases a list of the most popular articles we published over the course of the year based on readership.

The article that generated the most interest this year was one that looked at the policy of inclusion, or mainstreaming, in special education. A response to that article was our third most popular article of the year.

Some other popular articles were studies finding that teachers’ impact on non-cognitive skills is 10 times more predictive of students’ longer-term success than teachers’ impact on test scores; an analysis of the effectiveness of instructional coaching for teachers instead of regular professional development; and a look at whether teacher preparation programs can be evaluated based on the learning gains of their graduates’ students.

Other articles collected data on public support for higher teacher pay and greater school spending, the decline in private school attendance by middle school families, and whether states are lowering their proficiency standards.

Here’s the list of 2018’s Top 20 articles…

 

 

AI Now Law and Policy Reading List — from medium.com by the AI Now Institute

Excerpt:

Data-driven technologies are widely used in society to make decisions that affect many critical aspects of our lives, from health, education, employment, and criminal justice to economic, social and political norms. Their varied applications, uses, and consequences raise a number of unique and complex legal and policy concerns. As a result, it can be hard to figure out not only how these systems work but what to do about them.

As a starting point, AI Now offers this Law and Policy Reading List tailored for those interested in learning about key concepts, debates, and leading analysis on law and policy issues related to artificial intelligence and other emerging data-driven technologies.

 

Uber and Lyft drivers’ median hourly wage is just $3.37, report finds — from theguardian.com by Sam Levin
Majority of drivers make less than minimum wage and many end up losing money, according to study published by MIT

Excerpt (emphasis DSC):

Uber and Lyft drivers in the US make a median profit of $3.37 per hour before taxes, according to a new report that suggests a majority of ride-share workers make below minimum wage and that many actually lose money.

Researchers did an analysis of vehicle cost data and a survey of more than 1,100 drivers for the ride-hailing companies for the paper published by the Massachusetts Institute of Technology’s Center for Energy and Environmental Policy Research. The report – which factored in insurance, maintenance, repairs, fuel and other costs – found that 30% of drivers are losing money on the job and that 74% earn less than the minimum wage in their states.

The findings have raised fresh concerns about labor standards in the booming sharing economy as companies such as Uber and Lyft continue to face scrutiny over their treatment of drivers, who are classified as independent contractors and have few rights or protections.

“This business model is not currently sustainable,” said Stephen Zoepf, executive director of the Center for Automotive Research at Stanford University and co-author of the paper. “The companies are losing money. The businesses are being subsidized by [venture capital] money … And the drivers are essentially subsidizing it by working for very low wages.”

 


 

From DSC:
I don’t know enough about this to offer much feedback and/or insights on this sort of thing yet. But while it’s a bit too early for me to tell — and though I’m not myself a driver for Uber or Lyft — this article prompts me to put this type of thing on my radar.

That is, will the business models that arise from such a sharing economy only benefit a handful of owners or upper level managers or will such new business models benefit the majority of their employees? I’m very skeptical in these early stages though, as there aren’t likely medical or dental benefits, retirement contributions, etc. being offered to their employees with these types of companies. It likely depends upon the particular business model(s) and/or organization(s) being considered, but I think that it’s worth many of us watching this area.

 


 

Also see:

The Economics of Ride-Hailing: Driver Revenue, Expenses and Taxes— from ceepr.mit.edu / MIT Center for Energy and Environmental Policy Research by Stephen Zoepf, Stella Chen, Paa Adu, and Gonzalo Pozo

February 2018

We perform a detailed analysis of Uber and Lyft ride-hailing driver economics by pairing results from a survey of over 1100 drivers with detailed vehicle cost information. Results show that per hour worked, median profit from driving is $3.37/hour before taxes, and 74% of drivers earn less than the minimum wage in their state. 30% of drivers are actually losing money once vehicle expenses are included. On a per-mile basis, median gross driver revenue is $0.59/mile but vehicle operating expenses reduce real driver profit to a median of $0.29/mile. For tax purposes the $0.54/mile standard mileage deduction in 2016 means that nearly half of drivers can declare a loss on their taxes. If drivers are fully able to capitalize on these losses for tax purposes, 73.5% of an estimated U.S. market $4.8B in annual ride-hailing driver profit is untaxed.

Keywords: Transportation, Gig Economy, Cost-Bene t Analysis, Tax policy, Labor Center
Full Paper
| Research Brief

 

——-

Addendum on 3/7/18:

The ride-hailing wage war continues

How much do Lyft and Uber drivers really make? After reporting in a study that their median take-home pay was just 3.37/hour—and then getting called out by Uber’s CEO—researchers have significantly revised their findings.

Closer to a living wage: Lead author Stephen Zoepf of Stanford University released a statement on Twitter saying that using two different methods to recalculate the hourly wage, they find a salary of either $8.55 or $10 per hour, after expenses. Zoepf’s team will be doing a larger revision of the paper over the next few weeks.

Still low-balling it?: Uber and Lyft are adamant that even the new numbers underestimate what drivers are actually paid. “While the revised results are not as inaccurate as the original findings, driver earnings are still understated,” says Lyft’s director of communications Adrian Durbin.

The truth is out there: Depending on who’s doing the math, estimates range from $8.55 (Zoepf, et al.) up to over $21 an hour (Uber). In other words, we’re nowhere near a consensus on how much drivers in the gig-economy make.

 ——-

 

Tech companies should stop pretending AI won’t destroy jobs — from technologyreview.com / MIT Technology Review by Kai-Fu Lee
No matter what anyone tells you, we’re not ready for the massive societal upheavals on the way.

Excerpt (emphasis DSC):

The rise of China as an AI superpower isn’t a big deal just for China. The competition between the US and China has sparked intense advances in AI that will be impossible to stop anywhere. The change will be massive, and not all of it good. Inequality will widen. As my Uber driver in Cambridge has already intuited, AI will displace a large number of jobs, which will cause social discontent. Consider the progress of Google DeepMind’s AlphaGo software, which beat the best human players of the board game Go in early 2016. It was subsequently bested by AlphaGo Zero, introduced in 2017, which learned by playing games against itself and within 40 days was superior to all the earlier versions. Now imagine those improvements transferring to areas like customer service, telemarketing, assembly lines, reception desks, truck driving, and other routine blue-collar and white-­collar work. It will soon be obvious that half of our job tasks can be done better at almost no cost by AI and robots. This will be the fastest transition humankind has experienced, and we’re not ready for it.

And finally, there are those who deny that AI has any downside at all—which is the position taken by many of the largest AI companies. It’s unfortunate that AI experts aren’t trying to solve the problem. What’s worse, and unbelievably selfish, is that they actually refuse to acknowledge the problem exists in the first place.

These changes are coming, and we need to tell the truth and the whole truth. We need to find the jobs that AI can’t do and train people to do them. We need to reinvent education. These will be the best of times and the worst of times. If we act rationally and quickly, we can bask in what’s best rather than wallow in what’s worst.

 

From DSC:
If a business has a choice between hiring a human being or having the job done by a piece of software and/or by a robot, which do you think they’ll go with? My guess? It’s all about the money — whichever/whomever will be less expensive will get the job.

However, that way of thinking may cause enormous social unrest if the software and robots leave human beings in the (job search) dust. Do we, as a society, win with this way of thinking? To me, it’s capitalism gone astray. We aren’t caring enough for our fellow members of the human race, people who have to put bread and butter on their tables. People who have to support their families. People who want to make solid contributions to society and/or to pursue their vocation/callings — to have/find purpose in their lives.

 

Others think we’ll be saved by a universal basic income. “Take the extra money made by AI and distribute it to the people who lost their jobs,” they say. “This additional income will help people find their new path, and replace other types of social welfare.” But UBI doesn’t address people’s loss of dignity or meet their need to feel useful. It’s just a convenient way for a beneficiary of the AI revolution to sit back and do nothing.

 

 

To Fight Fatal Infections, Hospitals May Turn to Algorithms — from scientificamerican.com by John McQuaid
Machine learning could speed up diagnoses and improve accuracy

Excerpt:

The CDI algorithm—based on a form of artificial intelligence called machine learning—is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experimentation, machine learning’s predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University’s Clinical Inference and Algorithms Program.

“The implications of machine learning are profound,” Syed said. “Yet it also promises to be an unpredictable, disruptive force—likely to alter the way medical decisions are made and put some people out of work.

 

 

Lawyer-Bots Are Shaking Up Jobs — from technologyreview.com by Erin Winick

Excerpt:

Meticulous research, deep study of case law, and intricate argument-building—lawyers have used similar methods to ply their trade for hundreds of years. But they’d better watch out, because artificial intelligence is moving in on the field.

As of 2016, there were over 1,300,000 licensed lawyers and 200,000 paralegals in the U.S. Consultancy group McKinsey estimates that 22 percent of a lawyer’s job and 35 percent of a law clerk’s job can be automated, which means that while humanity won’t be completely overtaken, major businesses and career adjustments aren’t far off (see “Is Technology About to Decimate White-Collar Work?”). In some cases, they’re already here.

 

“If I was the parent of a law student, I would be concerned a bit,” says Todd Solomon, a partner at the law firm McDermott Will & Emery, based in Chicago. “There are fewer opportunities for young lawyers to get trained, and that’s the case outside of AI already. But if you add AI onto that, there are ways that is advancement, and there are ways it is hurting us as well.”

 

So far, AI-powered document discovery tools have had the biggest impact on the field. By training on millions of existing documents, case files, and legal briefs, a machine-learning algorithm can learn to flag the appropriate sources a lawyer needs to craft a case, often more successfully than humans. For example, JPMorgan announced earlier this year that it is using software called Contract Intelligence, or COIN, which can in seconds perform document review tasks that took legal aides 360,000 hours.

People fresh out of law school won’t be spared the impact of automation either. Document-based grunt work is typically a key training ground for first-year associate lawyers, and AI-based products are already stepping in. CaseMine, a legal technology company based in India, builds on document discovery software with what it calls its “virtual associate,” CaseIQ. The system takes an uploaded brief and suggests changes to make it more authoritative, while providing additional documents that can strengthen a lawyer’s arguments.

 

 

Lessons From Artificial Intelligence Pioneers — from gartner.com by Christy Pettey

CIOs are struggling to accelerate deployment of artificial intelligence (AI). A recent Gartner survey of global CIOs found that only 4% of respondents had deployed AI. However, the survey also found that one-fifth of the CIOs are already piloting or planning to pilot AI in the short term.

Such ambition puts these leaders in a challenging position. AI efforts are already stressing staff, skills, and the readiness of in-house and third-party AI products and services. Without effective strategic plans for AI, organizations risk wasting money, falling short in performance and falling behind their business rivals.

Pursue small-scale plans likely to deliver small-scale payoffs that will offer lessons for larger implementations

“AI is just starting to become useful to organizations but many will find that AI faces the usual obstacles to progress of any unproven and unfamiliar technology,” says Whit Andrews, vice president and distinguished analyst at Gartner. “However, early AI projects offer valuable lessons and perspectives for enterprise architecture and technology innovation leaders embarking on pilots and more formal AI efforts.”

So what lessons can we learn from these early AI pioneers?

 

 

Why Artificial Intelligence Researchers Should Be More Paranoid — from wired.com by Tom Simonite

Excerpt:

What to do about that? The report’s main recommendation is that people and companies developing AI technology discuss safety and security more actively and openly—including with policymakers. It also asks AI researchers to adopt a more paranoid mindset and consider how enemies or attackers might repurpose their technologies before releasing them.

 

 

How to Prepare College Graduates for an AI World — from wsj.com by
Northeastern University President Joseph Aoun says schools need to change their focus, quickly

Excerpt:

WSJ: What about adults who are already in the workforce?

DR. AOUN: Society has to provide ways, and higher education has to provide ways, for people to re-educate themselves, reskill themselves or upskill themselves.

That is the part that I see that higher education has not embraced. That’s where there is an enormous opportunity. We look at lifelong learning in higher education as an ancillary operation, as a second-class operation in many cases. We dabble with it, we try to make money out of it, but we don’t embrace it as part of our core mission.

 

 

Inside Amazon’s Artificial Intelligence Flywheel — from wired.com by Steven Levy
How deep learning came to power Alexa, Amazon Web Services, and nearly every other division of the company.

Excerpt:

Amazon loves to use the word flywheel to describe how various parts of its massive business work as a single perpetual motion machine. It now has a powerful AI flywheel, where machine-learning innovations in one part of the company fuel the efforts of other teams, who in turn can build products or offer services to affect other groups, or even the company at large. Offering its machine-learning platforms to outsiders as a paid service makes the effort itself profitable—and in certain cases scoops up yet more data to level up the technology even more.

 

 

 

 

Career Pathways: Five Ways to Connect College and Careers calls for states to help students, their families, and employers unpack the meaning of postsecondary credentials and assess their value in the labor market.

Excerpt:

If students are investing more to go to college, they need to have answers to basic questions about the value of postsecondary education. They need better information to make decisions that have lifelong economic consequences.

Getting a college education is one of the biggest investments people will make in their lives, but the growing complexity of today’s economy makes it difficult for higher education to deliver efficiency and consistent quality. Today’s economy is more intricate than those of decades past.

 

From this press release:

It’s Time to Fix Higher Education’s Tower of Babel, Says Georgetown University Report
The lack of transparency around college and careers leads to costly, uninformed decisions

(Washington, D.C., July 11, 2017) — A new report from the Georgetown University Center on Education and the Workforce (Georgetown Center), Career Pathways: Five Ways to Connect College and Careers, calls for states to help students, their families, and employers unpack the meaning of postsecondary credentials and assess their value in the labor market.

Back when a high school-educated worker could find a good job with decent wages, the question was simply whether or not to go to college. That is no longer the case in today’s economy, which requires at least some college to enter the middle class. The study finds that:

  • The number of postsecondary programs of study more than quintupled between 1985 and 2010 — from 410 to 2,260;
  • The number of colleges and universities more than doubled from 1,850 to 4,720 between 1950 and 2014; and
  • The number of occupations grew from 270 in 1950 to 840 in 2010.

The variety of postsecondary credentials, providers, and online delivery mechanisms has also multiplied rapidly in recent years, underscoring the need for common, measurable outcomes.

College graduates are also showing buyer’s remorse. While they are generally happy with their decision to attend college, more than half would choose a different major, go to a different college, or pursue a different postsecondary credential if they had a chance.

The Georgetown study points out that the lack of information drives the higher education market toward mediocrity. The report argues that postsecondary education and training needs to be more closely aligned to careers to better equip learners and workers with the skills they need to succeed in the 21st century economy and close the skills gap.

The stakes couldn’t be higher for students to make the right decisions. Since 1980, tuition and fees at public four year colleges and universities have grown 19 times faster than family incomes. Students and families want — and need — to know the value they are getting for their investment.

 

 



Also see:

  • Trumping toward college transparency — from linkedin.com by Anthony Carnevale
    The perfect storm is gathering around the need to increase transparency around college and careers. And in accordance with how public policy generally comes about, it might just happen. 


 

 

 

MITReport-OnlineEducation-April2016

 

chargeofMITOEPI-april2016

 

The final report of Massachusetts Institute of Technology’s Online Education Policy Initiative presents findings from discussions among the members of the Institute-wide initiative supported by advice from the advisory group. The report reflects comments and responses received from many sources, including education experts, government education officials, and representatives of university organizations.

 

 

Our findings target four areas: interdisciplinary collaboration, online educational technologies, the profession of the learning engineer, and institutional and organizational change. Focused attention in these areas could significantly advance our understanding of the opportunities and challenges in transforming education.

 

Recommendation 1:
Increase Interdisciplinary Collaboration Across Fields of Research in Higher Education, Using an Integrated Research Agenda

Recommendation 2:
Promote Online as an Important Facilitator in Higher Education

Recommendation 3:
Support the Expanding Profession of the “Learning Engineer”

Recommendation 4:
Foster Institutional and Organizational Change in Higher Education to Implement These Reforms

 

 

 

Also see:
MIT releases online education policy initiative report — from news.mit.edu by Jessica Fujimori, April 1, 2016
New report draws on diverse fields to reflect on digital learning.

Excerpts:

A new MIT report on online education policy draws on diverse fields, from socioeconomics to cognitive science, to analyze the current state of higher education and consider how advances in learning science and online technology might shape its future.

Titled “Online Education: A Catalyst for Higher Education Reform,” the report presents four overarching recommendations, stressing the importance of interdisciplinary collaboration, integration between online and traditional learning, a skilled workforce specializing in digital learning design, and high-level institutional and organizational change.

“There’s so much going on in online education, and it’s moving so quickly, that it’s important to take time to reflect,” says Eric Klopfer, a key participant in the initiative, who is a professor of education and directs the MIT Scheller Teacher Education Program. “One of the goals of the report is to try to help frame the discussion and to pull together some of the pieces of the conversation that are taking place in different arenas but are not necessarily considered in an integrated way,” Willcox says.

 

“We believe that there is a new category of professionals emerging from all this,” Sarma says. “We use the term ‘learning engineer,’ but maybe it’s going to be some other term — who knows?”

These “learning engineers” would have expertise in a discipline as well as in learning science and educational technologies, and would integrate knowledge across fields to design and optimize learning experiences.

“It’s important that this cadre of professionals get recognized as a valuable profession and provided with opportunities for advancement,” Willcox says. “Without people like this, we’re not going to make a transformation in education.”

 

Finally, the report recommends mechanisms to stimulate high-level institutional and organizational change to support the transformation of the industry, such as nurturing change agents and role models, and forming thinking communities to evaluate reform options.

“Policy makers and decision makers at institutions need to be proactive in thinking about this,” says Willcox. “There’s a lot to be learned by looking at industries that have seen this kind of transformation, particularly transformations brought on by digital technologies.”

 

Some items from Bryan Alexander:

 

 

Excerpt:

Today’s students expect their entire experience with an institution to mirror what they see from major online retailers and service providers; personalized, supportive and flexible. However, institutions are having to deliver on these heightened expectations with smaller budgets and less capacity to increase prices than ever before.

Scaling is absolutely critical for higher education institutions in today’s marketplace. Through scaling, institutions can “do more with less”—they can meet the sky-high expectations of today’s discerning students while keeping their costs and prices low.

This Feature highlights some of the approaches today’s institutions are taking to achieving scale.

 

 

A new vision for paying for higher education — from usnews.com by Lauren Camera
How do you build the federal student loan system from the ground up?

Excerpt:

As it stands now, the current system for financing higher education is particularly unfair for poor students, many of whom are forced to borrow more money than their wealthier peers, graduate at a much lower rate and go into default at a much higher rate.

To be sure, the average six-year graduation rate for students seeking a bachelor’s degree is 59.4 percent, but a recent survey of more than 1,000 public and private four-year colleges found that only 51 percent of Pell recipients graduate. And at community colleges, only 23 percent of first-time, full-time students ever receive a degree.

 

 

Is it time for colleges to withdraw from their outdated schedules? — from pri.org by Caroline Lester

Excerpt:

We asked a few college grads what they’d like to change about the current system. Their answers spanned from increasing accessibility, to eliminating lectures, to creating greater support services for students at risk of dropping out.

That last point is key: the vast majority of students who start college don’t graduate.

Community college, state schools, and private universities — six-year completion rates are falling. To Michael Crow, president of Arizona State University, this means something is wrong.

Crow believes that the best way to address America’s higher education woes is to lower the cost of a college education while personalizing teaching. He proposes three big changes…

 

 

Credentialing, free tuition top this week’s news — from ecampusnews.com by Laura Devaney

 

 

 

Some items from Jeff Selingo:

JeffSelingo-Feb2016

 

 

A brief excerpt from newsletter from one of Michigan’s Senators, Debbie Stabenow:

62 percent of students in Michigan graduate #InTheRed with student loan debt. A student who graduated from a 4-year Michigan college or university in 2014 owes on average almost $30,000 in loans, making Michigan 9th in the country on average student loan debt.  Student loan debt in the United States is over $1.3 trillion and is the 2nd highest form of consumer debt.

 

 

…and back from March 2015:

 

RethinkingHE-March2015

 

 

 

Looking for something?

Use the form below to search the site:

Still not finding what you're looking for? Drop a comment on a post or contact us so we can take care of it!

© 2019 | Daniel Christian