How blockbuster MOOCs could shape the future of teaching — from edsurge.com by Jeff Young

Excerpt:

There isn’t a New York Times bestseller list for online courses, but perhaps there should be. After all, so-called MOOCs, or massive open online courses, were meant to open education to as many learners as possible, and in many ways they are more like books (digital ones, packed with videos and interactive quizzes) than courses.

The colleges and companies offering MOOCs can be pretty guarded these days about releasing specific numbers on how many people enroll or pay for a “verified certificate” or microcredential showing they took the course. But both Coursera and EdX, two of the largest providers, do release lists of their most popular courses. And those lists offer a telling snapshot of how MOOCs are evolving and what their impact is on the instructors and institutions offering them.

Here are the top 10 most popular courses for each provider:

 

Coursera Top 10 Most Popular Courses (over past 12 months)

 

edX Top 10 Most Popular Courses (all time)

 

 

So what are these blockbuster MOOCs, then? Experiential textbooks? Gateways to more rigorous college courses? A new kind of entertainment program?

Maybe the answer is: all of the above.

 

 

 

Reimagining the Higher Education Ecosystem — from edu2030.agorize.com
How might we empower people to design their own learning journeys so they can lead purposeful and economically stable lives?

Excerpts:

The problem
Technology is rapidly transforming the way we live, learn, and work. Entirely new jobs are emerging as others are lost to automation. People are living longer, yet switching jobs more often. These dramatic shifts call for a reimagining of the way we prepare for work and life—specifically, how we learn new skills and adapt to a changing economic landscape.

The changes ahead are likely to hurt most those who can least afford to manage them: low-income and first generation learners already ill-served by our existing postsecondary education system. Our current system stifles economic mobility and widens income and achievement gaps; we must act now to ensure that we have an educational ecosystem flexible and fair enough to help all people live purposeful and economically stable lives. And if we are to design solutions proportionate to this problem, new technologies must be called on to scale approaches that reach the millions of vulnerable people across the country.

 

The challenge
How might we empower people to design their own learning journeys so they can lead purposeful and economically stable lives?

The Challenge—Reimagining the Higher Education Ecosystem—seeks bold ideas for how our postsecondary education system could be reimagined to foster equity and encourage learner agency and resilience. We seek specific pilots to move us toward a future in which all learners can achieve economic stability and lead purposeful lives. This Challenge invites participants to articulate a vision and then design pilot projects for a future ecosystem that has the following characteristics:

Expands access: The educational system must ensure that all people—including low-income learners who are disproportionately underserved by the current higher education system—can leverage education to live meaningful and economically stable lives.

Draws on a broad postsecondary ecosystem: While college and universities play a vital role in educating students, there is a much larger ecosystem in which students learn. This ecosystem includes non-traditional “classes” or alternative learning providers, such as MOOCs, bootcamps, and online courses as well as on-the-job training and informal learning. Our future learning system must value the learning that happens in many different environments and enable seamless transitions between learning, work, and life.

 

From DSC:
This is where I could see a vision similar to Learning from the Living [Class] Room come into play. It would provide a highly affordable, accessible platform, that would offer more choice, and more control to learners of all ages. It would be available 24×7 and would be a platform that supports lifelong learning. It would combine a variety of AI-enabled functionalities with human expertise, teaching, training, motivation, and creativity.

It could be that what comes out of this challenge will lay the groundwork for a future, massive new learning platform.

 

The Living [Class] Room -- by Daniel Christian -- July 2012 -- a second device used in conjunction with a Smart/Connected TV

 

Also see:

 

Computers that never forget a face — from Future Today Institute

Excerpts:

In August, the U.S. Customs and Border Protection will roll out new technology that will scan the faces of drivers as they enter and leave the United States. For years, accomplishing that kind of surveillance through a car windshield has been difficult. But technology is quickly advancing. This system, activated by ambient light sensors, range finders and remote speedometers, uses smart cameras and AI-powered facial recognition technology to compare images in government files with people behind the wheel.

Biometric borders are just the beginning. Faceprints are quickly becoming our new fingerprints, and this technology is marching forward with haste. Faceprints are now so advanced that machine learning algorithms can recognize your unique musculatures and bone structures, capillary systems, and expressions using thousands of data points. All the features that make up a unique face are being scanned, captured and analyzed to accurately verify identities. New hairstyle? Plastic surgery? They don’t interfere with the technology’s accuracy.

Why you should care. Faceprints are already being used across China for secure payments. Soon, they will be used to customize and personalize your digital experiences. Our Future Today Institute modeling shows myriad near-future applications, including the ability to unlock your smart TV with your face. Retailers will use your face to personalize your in-store shopping experience. Auto manufacturers will start using faceprints to detect if drivers are under the influence of drugs or alcohol and prevent them from driving. It’s plausible that cars will soon detect if a driver is distracted and take the wheel using an auto-pilot feature. On a diet but live with others? Stash junk food in a drawer and program the lock to restrict your access. Faceprints will soon create opportunities for a wide range of sectors, including military, law enforcement, retail, manufacturing and security. But as with all technology, faceprints could lead to the loss of privacy and widespread surveillance.

It’s possible for both risk and opportunity to coexist. The point here is not alarmist hand-wringing, or pointless calls for cease-and-desist demands on the development and use of faceprint technology. Instead, it’s to acknowledge an important emerging trend––faceprints––and to think about the associated risks and opportunities for you and your organization well in advance. Approach biometric borders and faceprints with your (biometrically unique) eyes wide open.

Near-Futures Scenarios (2018 – 2028):

OptimisticFaceprints make us safer, and they bring us back to physical offices and stores.  

Pragmatic: As faceprint adoption grows, legal challenges mount. 
In April, a U.S. federal judge ruled that Facebook must confront a class-action lawsuit that alleges its faceprint technology violates Illinois state privacy laws. Last year, a U.S. federal judge allowed a class-action suit to go forth against Shutterfly, claiming the company violated the Illinois Biometric Information Privacy Act, which ensures companies receive written releases before collecting biometric data, including faces. Companies and device manufacturers, who are early developers but late to analyzing legal outcomes, are challenged to balance consumer privacy with new security benefits.

CatastrophicFaceprints are used for widespread surveillance and authoritative control.

 

 

 

How AI is helping sports teams scout star play — from nbcnews.com by Edd Gent
Professional baseball, basketball and hockey are among the sports now using AI to supplement traditional coaching and scouting.

 

 

 

Preparing students for workplace of the future  — from educationdive.com by Shalina Chatlani

Excerpt:

The workplace of the future will be marked by unprecedentedly advanced technologies, as well as a focus on incorporating artificial intelligence to drive higher levels of production with fewer resources. Employers and education stakeholders, noting the reality of this trend, are turning a reflective eye toward current students and questioning whether they will be workforce ready in the years to come.

This has become a significant concern for higher education executives, who find their business models could be disrupted as they fail to meet workforce demands. A 2018 Gallup-Northeastern University survey shows that of 3,297 U.S. citizens interviewed, only 22% with a bachelor’s degree said their education left them “well” or “very well prepared” to use AI in their jobs.

In his book “Robot-Proof: Higher Education in the Age of Artificial Intelligence,” Northeastern University President Joseph Aoun argued that for higher education to adapt advanced technologies, it has to focus on life-long learning, which he said says prepares students for the future by fostering purposeful integration of technical literacies, such as coding and data literacy, with human literacies, such as creativity, ethics, cultural agility and entrepreneurship.

“When students combine these literacies with experiential components, they integrate their knowledge with real life settings, leading to deep learning,” Aoun told Forbes.

 

 

Amazon’s A.I. camera could help people with memory loss recognize old friends and family — from cnbc.com by Christina Farr

  • Amazon’s DeepLens is a smart camera that can recognize objects in front of it.
  • One software engineer, Sachin Solkhan, is trying to figure out how to use it to help people with memory loss.
  • Users would carry the camera to help them recognize people they know.

 

 

Microsoft acquired an AI startup that helps it take on Google Duplex — from qz.com by Dave Gershgorn

Excerpt:

We’re going to talk to our technology, and everyone else’s too. Google proved that earlier this month with a demonstration of artificial intelligence that can hop on the phone to book a restaurant reservation or appointment at the hair salon.

Now it’s just a matter of who can build that technology fastest. To reach that goal, Microsoft has acquired conversational AI startup Semantic Machines for an undisclosed amount. Founded in 2014, the startup’s goal was to build AI that can converse with humans through speech or text, with the ability to be trained to converse on any language or subject.

 

 

Researchers developed an AI to detect DeepFakes — from thenextweb.com by Tristan Greene

Excerpt:

A team of researchers from the State University of New York (SUNY) recently developed a method for detecting whether the people in a video are AI-generated. It looks like DeepFakes could meet its match.

What it means: Fear over whether computers will soon be able to generate videos that are indistinguishable from real footage may be much ado about nothing, at least with the currently available methods.

The SUNY team observed that the training method for creating AI that makes fake videos involves feeding it images – not video. This means that certain human physiological quirks – like breathing and blinking – don’t show up in computer-generated videos. So they decided to build an AI that uses computer vision to detect blinking in fake videos.

 

 

Bringing It Down To Earth: Four Ways Pragmatic AI Is Being Used Today — from forbes.com by Carlos Melendez

Excerpt:

Without even knowing it, we are interacting with pragmatic AI day in and day out. It is used in the automated chatbots that answer our calls and questions and the customer service rep that texts with us on a retail site, providing a better and faster customer experience.

Below are four key categories of pragmatic AI and ways they are being applied today.

1. Speech Recognition And Natural Language Processing (NLP)
2. Predictive Analytics
3. Image Recognition And Computer Vision
4. Self-Driving Cars And Robots

 

 

Billable Hour ‘Makes No Sense’ in an AI World — from biglawbusiness.com by Helen Gunnarsson

Excerpt:

Artificial intelligence (AI) is transforming the practice of law, and “data is the new oil” of the legal industry, panelist Dennis Garcia said at a recent American Bar Association conference.Garcia is an assistant general counsel for Microsoft in Chicago. Robert Ambrogi, a Massachusetts lawyer and blogger who focuses on media, technology, and employment law, moderated the program.“The next generation of lawyers is going to have to understand how AI works” as part of the duty of competence, panelist Anthony E. Davis told the audience. Davis is a partner with Hinshaw & Culbertson LLP in New York.

Davis said AI will result in dramatic changes in law firms’ hiring and billing, among other things. The hourly billing model, he said, “makes no sense in a universe where what clients want is judgment.” Law firms should begin to concern themselves not with the degrees or law schools attended by candidates for employment but with whether they are “capable of developing judgment, have good emotional intelligence, and have a technology background so they can be useful” for long enough to make hiring them worthwhile, he said.

 

 

Deep Learning Tool Tops Dermatologists in Melanoma Detection — from healthitanalytics.com
A deep learning tool achieved greater accuracy than dermatologists when detecting melanoma in dermoscopic images.

 

 

Apple’s plans to bring AI to your phone — from wired.com by Tom Simonite

Excerpt:

HomeCourt is built on tools announced by Federighi last summer, when he launched Apple’s bid to become a preferred playground for AI-curious developers. Known as Core ML, those tools help developers who’ve trained machine learning algorithms deploy them on Apple’s mobile devices and PCs.

At Apple’s Worldwide Developer Conference on Monday, Federighi revealed the next phase of his plan to enliven the app store with AI. It’s a tool called Create ML that’s something like a set of training wheels for building machine learning models in the first place. In a demo, training an image-recognition algorithm to distinguish different flavors of ice cream was as easy as dragging and dropping a folder containing a few dozen images and waiting a few seconds. In a session for developers, Apple engineers suggested Create ML could teach software to detect whether online comments are happy or angry, or predict the quality of wine from characteristics such as acidity and sugar content. Developers can use Create ML now but can’t ship apps using the technology until Apple’s latest operating systems arrive later this year.

 

 

 

From DSC:
I found the following graphic out at a posting entitled, Continuous Learning & Development; more than just continuous training (from modernworkplacelearning.com/magazine). I thought it was an excellent example of a learning ecosystem!

 

 

 

 

From DSC:
I just found out about the work going out at LearningScientists.org.

I was very impressed after my initial review of their materials! What I really appreciate about their work is that they are serious in identifying some highly effective means of how we learn best — pouring over a great deal of research in order to do so. But they don’t leave things there. They help translate that research into things that teachers can then try out in the classroom. This type of practical, concrete help is excellent and needed!

  • Daniel Willingham and some of his colleagues take research and help teachers apply it as well
  • Another person who does this quite well is Pooja Agarwal, an Assistant Professor, Cognitive Scientist, & former K-12 Teacher. Pooja is teaming up with Patrice Bain to write a forthcoming book entitled, Powerful Teaching: Unleash the Science of Learning!  She founded and operates the RetrievalPractice.org site.)

From the LearningScientists.org website (emphasis DSC):

We are cognitive psychological scientists interested in research on education. Our main research focus is on the science of learning. (Hence, “The Learning Scientists”!)

Our Vision is to make scientific research on learning more accessible to students, teachers, and other educators.

Click the button below to learn more about us. You can also check out our social media pages: FacebookTwitterInstagram, & Tumblr.

 

They have a solid blog, podcast, and some valuable downloadable content.

 

 

 

In the downloadable content area, the posters that they’ve created (or ones like them) should be posted at every single facility where learning occurs — K-12 schools, community colleges, colleges, universities, libraries of all kinds, tutoring centers, etc. It may be that such posters — and others like them that encourage the development of metacognitive skills of our students — are out there. I just haven’t run into them.

For example, here’s a poster on learning how to study using spaced practice:

 

 

 

 

Anyway, there’s some great work out there at LearningScientists.org!

 

 


Also relevant here, see:

 

 

 

 

Andrew Ng is probably teaching more students than anyone else on the planet. (Without a university involved.) — from edsurge.com by Jeff Young

Excerpt:

One selling point of MOOCs (massive online open courses) has been that students can access courses from the world’s most famous universities. The assumption—especially in the marketing messages from major providers like Coursera and edX—is that the winners of traditional higher education will also end up the winners in the world of online courses.

But that isn’t always happening.

In fact, three of the 10 most popular courses on Coursera aren’t produced by a college or university at all, but by a company. That company—called Deeplearning.ai—is a unique provider of higher education. It is essentially built on the reputation of its founder, Andrew Ng, who teaches all five of the courses it offers so far.

Ng is seen as one of the leading figures in artificial intelligence, having founded and directed the Google Brain project and served as the chief scientist at the Chinese search giant Baidu, as well as having directed the artificial intelligence laboratory at Stanford University. He also happens to be the co-founder of Coursera itself, and it was his Stanford course on machine learning that helped launch the MOOC craze in the first place.

In fact, Ng’s original Stanford MOOC remains the most popular course offered by Coursera. Since the course began in 2012, it has drawn more than 1.7 million enrollments. (It now runs on demand, so people can sign up anytime.) And his new series of courses through Deeplearning.ai, which kicked off last year, have already exceeded 250,000 signups. Even allowing for the famously low completion rates of MOOCs, it still means that hundreds of thousands of people have sat through lecture videos by Ng.

 

 

 

 

 

 

 

Below are some excerpted slides from her presentation…

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Also see:

  • 20 important takeaways for learning world from Mary Meeker’s brilliant tech trends – from donaldclarkplanb.blogspot.com by Donald Clark
    Excerpt:
    Mary Meeker’s slide deck has a reputation of being the Delphic Oracle of tech. But, at 294 slides it’s a lot to take in. Don’t worry, I’ve been through them all. It has tons on economic stuff that is of marginal interest to education and training but there’s plenty to to get our teeth into. We’re not immune to tech trends, indeed we tend to follow in lock-step, just a bit later than everyone else. Among the data are lots of fascinating insights that point the way forward in terms of what we’re likely to be doing over the next decade. So here’s a really quick, top-end summary for folk in the learning game.

 

“Educational content usage online is ramping fast” with over 1 billion daily educational videos watched. There is evidence that use of the Internet for informal and formal learning is taking off.

 

 

 

 

 

 

10 Big Takeaways From Mary Meeker’s Widely-Read Internet Report — from fortune.com by  Leena Rao

 

 

 

 

Skill shift: Automation and the future of the workforce — from mckinsey.com by Jacques Bughin, Eric Hazan, Susan Lund, Peter Dahlström, Anna Wiesinger, and Amresh Subramaniam
Demand for technological, social and emotional, and higher cognitive skills will rise by 2030. How will workers and organizations adapt?

Excerpt:

Skill shifts have accompanied the introduction of new technologies in the workplace since at least the Industrial Revolution, but adoption of automation and artificial intelligence (AI) will mark an acceleration over the shifts of even the recent past. The need for some skills, such as technological as well as social and emotional skills, will rise, even as the demand for others, including physical and manual skills, will fall. These changes will require workers everywhere to deepen their existing skill sets or acquire new ones. Companies, too, will need to rethink how work is organized within their organizations.

This briefing, part of our ongoing research on the impact of technology on the economy, business, and society, quantifies time spent on 25 core workplace skills today and in the future for five European countries—France, Germany, Italy, Spain, and the United Kingdom—and the United States and examines the implications of those shifts.

Topics include:
How will demand for workforce skills change with automation?
Shifting skill requirements in five sectors
How will organizations adapt?
Building the workforce of the future

 

 

 

“Retrieval practice” is a learning strategy where we focus on getting information out. Through the act of retrieval, or calling information to mind, our memory for that information is strengthened and forgetting is less likely to occur. Retrieval practice is a powerful tool for improving learning without more technology, money, or class time.

On this website (and in our free Retrieval Practice Guide), we discuss how to use retrieval practice to improve learning. Established by nearly 100 years of research, retrieval practice is a simple and powerful technique to transform teaching and learning.

In order to improve learning, we must approach it through a new lens – let’s focus not on getting information “in,” but on getting information “out.”

 

 

What is retrieval practice?
Retrieval practice is a strategy in which bringing information to mind enhances and boosts learning. Deliberately recalling information forces us to pull our knowledge “out” and examine what we know.

For instance, recalling an answer to a science question improves learning to a greater extent than looking up the answer in a textbook. And having to actually recall and write down an answer to a flashcard improves learning more than thinking that you know the answer and flipping the card over prematurely.

Often, we think we’ve learned some piece of information, but we come to realize we struggle when we try to recall the answer. It’s precisely this “struggle” or challenge that improves our memory and learning – by trying to recall information, we exercise or strengthen our memory, and we can also identify gaps in our learning.

Note that cognitive scientists used to refer to retrieval practice as “the testing effect.” Prior research examined the fascinating finding that tests (or short quizzes) dramatically improve learning. More recently, researchers have demonstrated that more than simply tests and quizzes improve learning: flashcards, practice problems, writing prompts, etc. are also powerful tools for improving learning. 

Whether this powerful strategy is called retrieval practice or the testing effect, it is important to keep in mind that the act of pulling information “out” from our minds dramatically improves learning, not the tests themselves. In other words retrieval is the active process we engage in to boost learning; tests and quizzes are merely methods to promote retrieval.

 

 

Also on that site:

 

 

Learn more about this valuable book with our:

 

 

Also on that site:

 

 

Excerpt from the Interleaved Mathematics Practice guide (on page 8 of 13):

Interleaved practice gives students a chance to choose a strategy.
When practice problems are arranged so that consecutive problems cannot be solved by the same strategy, students are forced to choose a strategy on the basis of the problem itself. This gives students a chance to both choose and use a strategy.

Interleaved practice works.
In several randomized control studies, students who received mostly interleaved practice scored higher on a final test than did students who received mostly blocked practice.

 

 

 



From DSC:
Speaking of resources regarding learning…why don’t we have posters in all of our schools, colleges, community colleges, universities, vocational training centers, etc. that talk about the most effective strategies to learn about new things?



 

 

 

The scary amount that college will cost in the future — from cnbc.com by Annie Nova

Excerpt:

Think college is expensive now? Then new parents will probably want to take a seat for this news.

In 2036, just 18 years from now, four years at a private university will be around $303,000, up from $167,000 today.

To get a degree at a public university you’ll need about $184,000, compared with $101,000 now.

These forecasts were provided by Wealthfront, an automated investment platform that offers college saving options. It uses Department of Education data on the current cost of schools along with expected annual inflation to come up with its projections.

 

Excerpted graphic:

 

From DSC:
We had better be at the end of the line of thinking that says these tuition hikes can continue. It’s not ok. More and more people will be shut out by this kind of societal gatekeeper. The ever-increasing cost of obtaining a degree has become a matter of social justice for me. Other solutions are needed. The 800 pound gorilla of debt that’s already being loaded onto more and more of our graduates will impact them for years…even for decades in many of our graduates’ cases.

It’s my hope that a variety of technologies will make learning more affordable, yet still provide a high quality of education. In fact, I’m hopeful that the personalization/customization of learning will take some major steps forward in the very near future. We will still need and want solid teachers, professors, and trainers, but I’m hopeful that those folks will be aided by the heavy lifting that will be done by some powerful tools/technologies that will be aimed at helping people learn and grow…providing lifelong learners with more choice, more control.

I love the physical campus as much as anyone, and I hope that all students can have that experience if they want it. But I’ve seen and worked with the high costs of building and maintaining physical spaces — maintaining our learning spaces, dorms, libraries, gyms, etc. is very expensive.

I see streams of content becoming more prevalent in the future — especially for lifelong learners who need to reinvent themselves in order to stay marketable. We will be able to subscribe and unsubscribe to curated streams of content that we want to learn more about. For example, today, that could involve RSS feeds and Feedly (to aggregate those feeds). I see us using micro-learning to help us encode information and then practice recalling it (i.e., spaced practice), to help us stop or lessen the forgetting curves we all experience, to help us sort information into things we know and things that we need more assistance on (while providing links to resources that will help us obtain better mastery of the subject(s)).

 

 

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