To higher ed: When the race track is going 180mph, you can’t walk or jog onto the track. [Christian]

From DSC:
When the race track is going 180mph, you can’t walk or jog onto the track.  What do I mean by that? 

Consider this quote from an article that Jeanne Meister wrote out at Forbes entitled, “The Future of Work: Three New HR Roles in the Age of Artificial Intelligence:”*

This emphasis on learning new skills in the age of AI is reinforced by the most recent report on the future of work from McKinsey which suggests that as many as 375 million workers around the world may need to switch occupational categories and learn new skills because approximately 60% of jobs will have least one-third of their work activities able to be automated.

Go scan the job openings and you will likely see many that have to do with technology, and increasingly, with emerging technologies such as artificial intelligence, deep learning, machine learning, virtual reality, augmented reality, mixed reality, big data, cloud-based services, robotics, automation, bots, algorithm development, blockchain, and more. 

 

From Robert Half’s 2019 Technology Salary Guide 

 

 

How many of us have those kinds of skills? Did we get that training in the community colleges, colleges, and universities that we went to? Highly unlikely — even if you graduated from one of those institutions only 5-10 years ago. And many of those institutions are often moving at the pace of a nice leisurely walk, with some moving at a jog, even fewer are sprinting. But all of them are now being asked to enter a race track that’s moving at 180mph. Higher ed — and society at large — are not used to moving at this pace. 

This is why I think that higher education and its regional accrediting organizations are going to either need to up their game hugely — and go through a paradigm shift in the required thinking/programming/curricula/level of responsiveness — or watch while alternatives to institutions of traditional higher education increasingly attract their learners away from them.

This is also, why I think we’ll see an online-based, next generation learning platform take place. It will be much more nimble — able to offer up-to-the minute, in-demand skills and competencies. 

 

 

The below graphic is from:
Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages

 

 

 


 

* Three New HR Roles To Create Compelling Employee Experiences
These new HR roles include:

  1. IBM: Vice President, Data, AI & Offering Strategy, HR
  2. Kraft Heinz Senior Vice President Global HR, Performance and IT
  3. SunTrust Senior Vice President Employee Wellbeing & Benefits

What do these three roles have in common? All have been created in the last three years and acknowledge the growing importance of a company’s commitment to create a compelling employee experience by using data, research, and predictive analytics to better serve the needs of employees. In each case, the employee assuming the new role also brought a new set of skills and capabilities into HR. And importantly, the new roles created in HR address a common vision: create a compelling employee experience that mirrors a company’s customer experience.

 


 

An excerpt from McKinsey Global Institute | Notes from the Frontier | Modeling the Impact of AI on the World Economy 

Workers.
A widening gap may also unfold at the level of individual workers. Demand for jobs could shift away from repetitive tasks toward those that are socially and cognitively driven and others that involve activities that are hard to automate and require more digital skills.12 Job profiles characterized by repetitive tasks and activities that require low digital skills may experience the largest decline as a share of total employment, from some 40 percent to near 30 percent by 2030. The largest gain in share may be in nonrepetitive activities and those that require high digital skills, rising from some 40 percent to more than 50 percent. These shifts in employment would have an impact on wages. We simulate that around 13 percent of the total wage bill could shift to categories requiring nonrepetitive and high digital skills, where incomes could rise, while workers in the repetitive and low digital skills categories may potentially experience stagnation or even a cut in their wages. The share of the total wage bill of the latter group could decline from 33 to 20 percent.13 Direct consequences of this widening gap in employment and wages would be an intensifying war for people, particularly those skilled in developing and utilizing AI tools, and structural excess supply for a still relatively high portion of people lacking the digital and cognitive skills necessary to work with machines.

 


 

 

Smart Machines & Human Expertise: Challenges for Higher Education — from er.educause.edu by Diana Oblinger

Excerpts:

What does this mean for higher education? One answer is that AI, robotics, and analytics become disciplines in themselves. They are emerging as majors, minors, areas of emphasis, certificate programs, and courses in many colleges and universities. But smart machines will catalyze even bigger changes in higher education. Consider the implications in three areas: data; the new division of labor; and ethics.

 

Colleges and universities are challenged to move beyond the use of technology to deliver education. Higher education leaders must consider how AI, big data, analytics, robotics, and wide-scale collaboration might change the substance of education.

 

Higher education leaders should ask questions such as the following:

  • What place does data have in our courses?
  • Do students have the appropriate mix of mathematics, statistics, and coding to understand how data is manipulated and how algorithms work?
  • Should students be required to become “data literate” (i.e., able to effectively use and critically evaluate data and its sources)?

Higher education leaders should ask questions such as the following:

  • How might problem-solving and discovery change with AI?
  • How do we optimize the division of labor and best allocate tasks between humans and machines?
  • What role do collaborative platforms and collective intelligence have in how we develop and deploy expertise?


Higher education leaders should ask questions such as the following:

  • Even though something is possible, does that mean it is morally responsible?
  • How do we achieve a balance between technological possibilities and policies that enable—or stifle—their use?
  • An algorithm may represent a “trade secret,” but it might also reinforce dangerous assumptions or result in unconscious bias. What kind of transparency should we strive for in the use of algorithms?

 

 

 

Experiences in self-determined learning — a free download/PDF file from uni-oldenburg.de by Lisa Blaschke, Chris Kenyon, & Stewart Hase (Eds.)

Excerpts (emphasis DSC):

An Introduction to Self-Determined Learning (Heutagogy)

Summary
There is a good deal that is provocative in the theory and principles surrounding self-determined learning or heutagogy. So, it seems appropriate to start off with a, hopefully, eyebrow-raising observation. One of the key ideas underpinning self-determined learning is that learning, and educational and training are quite different things. Humans are born to learn and are very good at it. Learning is a natural capability and it occurs across the human lifespan, from birth to last breath. In contrast, educational and training systems are concerned with the production of useful citizens, who can contribute to the collective economic good. Education and training is largely a conservative enterprise that is highly controlled, is product focused, where change is slow, and the status quo is revered. Learning, however, is a dynamic process intrinsic to the learner, uncontrolled except by the learner’s mental processes. Self-determined learning is concerned with understanding how people learn best and how the methods derived from this understanding can be applied to educational systems. This chapter provides a relatively brief introduction of the origins, the key principles, and the practice of self-determined learning. It also provides a number of resources to enable the interested reader to take learning about the approach further.

Contributors to this book come from around the world: they are everyday practitioners of self-determined learning who have embraced the approach. In doing so, they have chosen the path less taken and set off on a journey of exploration and discovery – a new frontier – as they implement heutagogy in their homes, schools, and workplaces. Each chapter was written with the intent of sharing the experiences of practical applications of heutagogy, while also encouraging those just starting out on the journey in using self-determined learning. The authors in this book are your guides as you move forward and share with you the lessons they have learned along the way. These shared experiences are meant to be read – or dabbled in – in any way that you want to read them. There is no fixed recipe or procedure for tackling the book contents.

At the heart of self-determined learning is that the learner is at the centre of the learning process. Learning is intrinsic to the learner, and the educator is but an agent, as are many of the resources so freely available these days. It is now so easy to access knowledge and skills (competencies), and in informal settings we do this all the time, and we do it well. Learning is complex and non-linear, despite what the curricula might try to dictate. In addition, every brain is different as a result of its experience (as brain research tells us). Each brain will also change as learning takes place with new hypotheses, new needs, and new questions forming, as new neuronal connections are created.

Heutagogy also doesn’t have anything directly to do with self-determination theory (SDT). SDT is a theory of motivation related to acting in healthy and effective ways (Ryan & Deci, 2000). However, heutagogy is related to the philosophical notion of self-determinism and shares a common belief in the role of human agency in behavior.

The idea of human agency is critical to self-determined learning, where learning is learner-directed. Human agency is the notion that humans have the capacity to make choices and decisions, and then act on them in the real world. However, how experiences and learning bring people to make the choices and decisions that they do make, and what actions they may then take is a very complex matter. What we are concerned with in self-determined learning is that people have agency with respect to how, what, and when they learn. It is something that is intrinsic to each individual person. Learning occurs in the learner’s brain, as the result of his or her past and present experiences.

 

The notion of placing the learner at the centre of the learning experience is a key principle of self-determined learning. This principle is the opposite of teacher-centric or, perhaps more accurately curriculum-centric, approaches to learning. This is not to say that the curriculum is not important, just that it needs to be geared to the learner – flexible, adaptable, and be a living document that is open to change.

Teacher-centric learning is an artifact of the industrial revolution when an education system was designed to meet the needs of the factories (Ackoff & Greenberg, 2008) and to “make the industrial wheel go around” (Hase & Kenyon, 2013b). It is time for a change to learner-centred learning and the time is right with easy access to knowledge and skills through the Internet, high-speed communication and ‘devices’. Education can now focus on more complex cognitive activities geared to the needs of the 21st century learner, rather than have its main focus on competence (Blaschke & Hase, 2014; Hase & Kenyon, 2013a).

 

 

 

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:

 

From DSC:
How do we best help folks impacted by these changes reinvent themselves? And to what? What adjustments to our educational systems do we need to make in order to help people stay marketable and employed?

Given the pace of change and the need for lifelong learning, we need to practice some serious design thinking on our new reality.

 


 

The amount of retail space closing in 2018 is on pace to break a record — from cnbc.com by Lauren Thomas

  • Bon-Ton’s more than 200 stores encompass roughly 24 million square feet.
  • CoStar Group has calculated already more than 90 million square feet of retail space (including Bon-Ton) is set to close in 2018.
  • That’s easily on track to surpass a record 105 million square feet of space shuttered in 2017.

 


 

 

 

Students are being prepared for jobs that no longer exist. Here’s how that could change. — from nbcnews.com by Sarah Gonser, The Hechinger Report
As automation disrupts the labor market and good middle-class jobs disappear, schools are struggling to equip students with future-proof skills.

Excerpts:

In many ways, the future of Lowell, once the largest textile manufacturing hub in the United States, is tied to the success of students like Ben Lara. Like many cities across America, Lowell is struggling to find its economic footing as millions of blue-collar jobs in manufacturing, construction and transportation disappear, subject to offshoring and automation.

The jobs that once kept the city prosperous are being replaced by skilled jobs in service sectors such as health care, finance and information technology — positions that require more education than just a high-school diploma, thus squeezing out many of those blue-collar, traditionally middle-class workers.

 

As emerging technologies rapidly and thoroughly transform the workplace, some experts predict that by 2030 400 million to 800 million people worldwide could be displaced and need to find new jobs. The ability to adapt and quickly acquire new skills will become a necessity for survival.

 

 

“We’re preparing kids for these jobs of tomorrow, but we really don’t even know what they are,” said Amy McLeod, the school’s director of curriculum, instruction and assessment. “It’s almost like we’re doing this with blinders on. … We’re doing all we can to give them the finite skills, the computer languages, the programming, but technology is expanding so rapidly, we almost can’t keep up.”

 

 

 

For students like Amber, who would rather do just about anything but go to school, the Pathways program serves another function: It makes learning engaging, maybe even fun, and possibly keeps her in school and on track to graduate.

“I think we’re turning kids off to learning in this country by putting them in rows and giving them multiple-choice tests — the compliance model,” McLeod said. “But my hope is that in the pathways courses, we’re teaching them to love learning. And they’re learning about options in the field — there’s plenty of options for kids to try here.”

 

 

 

From DSC:
After seeing the article entitled, “Scientists Are Turning Alexa into an Automated Lab Helper,” I began to wonder…might Alexa be a tool to periodically schedule & provide practice tests & distributed practice on content? In the future, will there be “learning bots” that a learner can employ to do such self-testing and/or distributed practice?

 

 

From page 45 of the PDF available here:

 

Might Alexa be a tool to periodically schedule/provide practice tests & distributed practice on content?

 

 

 

From DSC:
Regarding the article below…why did it take Udacity needing to team up with Infosys to offer this type of program and curriculum? Where are the programs in institutions of traditional higher education on this?  Are similar programs being developed? If so, how quickly will they come to market? I sure hope that such program development is in progress..and perhaps it is. But the article below goes to show us that alternatives to traditional higher education seem to be more responsive to the new, exponential pace of change that we now find ourselves in.

We have to pick up the pace! To do this, we need to identify any obstacles to our institutions adapting to this new pace of change — and then address them immediately. I see our current methods of accreditation as one of the areas that we need to address. We’ve got to get solid programs to market much faster!

And for those folks in higher ed who say change isn’t happening rapidly — that it’s all a bunch of hype — you likely still have a job. But you need to go talk with some people who don’t, or who’ve had their jobs recently impacted big time. Here are some suggestions of folks to talk with:

  • Taxi drivers who were impacted by Lyft and by Uber these last 5-10 years; they may still have their jobs, if they’re lucky. But they’ve been impacted big time…and are likely driving for Lyft and/or Uber as well as their former employers; they’re likely to have less bargaining power than they used to as the supply of drivers has skyrocketed. (By the way, the very existence of such organizations couldn’t have happened without the smartphone and mobile-related technologies/telecommunications.)
  • Current managers and former employees at hotels/motels about the impacts on their industry by AirBnB over a similar time frame
  • Hiring managers at law firms who’ve cut back on hiring entry-level lawyers…work that’s increasingly being done by software (example)
  • Employees who worked at brick and mortar retailers who have been crushed by Amazon.com’s online-based presence (in not that long of time, by the way). For example, below is what our local Sears store looks like these days…go find an employee who used to work at Sears or a Sears automotive-related store:

 

This is what our local Sears store looks like today

This picture is for those who say there is no disruption.
You call
this hype?!

 

The above example list — that’s admittedly woefully incomplete — doesn’t include the folks displaced by technology over the last several decades, such as:

  • Former bank tellers who lost their jobs to ATMs
  • Checkout clerks at the grocery stores who lost their jobs to self-service stations
  • Check-in agents at the airports who lost their jobs to self-service stations
  • Etc., etc., etc.

Institutions of traditional higher education
need to pick up the pace — big time!

 


Infosys and Udacity team up to train 500 engineers in autonomous technologies — from by Leah Brown
Infosys’ COO Ravi Kumar explains how these individuals can apply what they learn to other industries.

Excerpt (emphasis DSC):

Infosys, a global technology consulting firm, recently partnered with online learning platform Udacity to create a connected service that provides training for autonomous vehicles, and other services for B2B providers of autonomous vehicles.

TechRepublic’s Dan Patterson met with Infosys’ COO Ravi Kumar to discuss how autonomous technology can help create new industries.

Autonomous technology is going to be an emerging technology of the future, Kumar said. So Infosys and Udacity came together and developed a plan to train 500 engineers on autonomous technologies, and teach them how to apply it to other industries.

 

Per Wikipedia:
Udacity is a for-profit educational organization founded by Sebastian Thrun, David Stavens, and Mike Sokolsky offering massive open online courses (MOOCs). According to Thrun, the origin of the name Udacity comes from the company’s desire to be “audacious for you, the student.” While it originally focused on offering university-style courses, it now focuses more on vocational courses for professionals.

 


 

But times are changing. Artificial intelligence (AI) and robotics are facilitating the automation of a growing number of “doing” tasks. Today’s AI-enabled, information-rich tools are increasingly able to handle jobs that in the past have been exclusively done by people—think tax returns, language translations, accounting, even some kinds of surgery. These shifts will produce massive disruptions to employment and hold enormous implications for you as a business leader. (mckinsey.com)

 


 

 

 

 

 

 

Excerpt:

Artificial Intelligence has leapt to the forefront of global discourse, garnering increased attention from practitioners, industry leaders, policymakers, and the general public. The diversity of opinions and debates gathered from news articles this year illustrates just how broadly AI is being investigated, studied, and applied. However, the field of AI is still evolving rapidly and even experts have a hard time understanding and tracking progress across the field.

Without the relevant data for reasoning about the state of AI technology, we are essentially “flying blind” in our conversations and decision-making related to AI.

Created and launched as a project of the One Hundred Year Study on AI at Stanford University (AI100), the AI Index is an open, not-for-profit project to track activity and progress in AI. It aims to facilitate an informed conversation about AI that is grounded in data. This is the inaugural annual report of the AI Index, and in this report we look at activity and progress in Artificial Intelligence through a range of perspectives. We aggregate data that exists freely on the web, contribute original data, and extract new metrics from combinations of data series.

All of the data used to generate this report will be openly available on the AI Index website at aiindex.org. Providing data, however, is just the beginning. To become truly useful, the AI Index needs support from a larger community. Ultimately, this report is a call for participation. You have the ability to provide data, analyze collected data, and make a wish list of what data you think needs to be tracked. Whether you have answers or questions to provide, we hope this report inspires you to reach out to the AI Index and become part of the effort to ground the conversation about AI.

 

 

 

The Ivory Tower Can’t Keep Ignoring Tech — from nytimes.com by Cathy O’Neil

Excerpt:

We need academia to step up to fill in the gaps in our collective understanding about the new role of technology in shaping our lives. We need robust research on hiring algorithms that seem to filter out people with mental health disorders, sentencing algorithms that fail twice as often for black defendants as for white defendants, statistically flawed public teacher assessments or oppressive scheduling algorithms. And we need research to ensure that the same mistakes aren’t made again and again. It’s absolutely within the abilities of academic research to study such examples and to push against the most obvious statistical, ethical or constitutional failures and dedicate serious intellectual energy to finding solutions. And whereas professional technologists working at private companies are not in a position to critique their own work, academics theoretically enjoy much more freedom of inquiry.

 

 

There is essentially no distinct field of academic study that takes seriously the responsibility of understanding and critiquing the role of technology — and specifically, the algorithms that are responsible for so many decisions — in our lives.

 

 

There’s one solution for the short term. We urgently need an academic institute focused on algorithmic accountability. First, it should provide a comprehensive ethical training for future engineers and data scientists at the undergraduate and graduate levels, with case studies taken from real-world algorithms that are choosing the winners from the losers. Lecturers from humanities, social sciences and philosophy departments should weigh in.

 

 

 

Somewhat related:

 

 

 

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