Defining Online: Ask the Machines?

Dave Weinberger had a very interesting post on Backchannel last week that suggest AI now has knowledge we will never understand.  Dave noted:

“We are increasingly relying on machines that derive conclusions from models that they themselves created, models that are often beyond human comprehension, models that “think” about the world differently than we do.”

He goes on to say that we have long been trying to simplify the world – think Einstein attempting to find a Unified Field Theory to tie together relativity, electromagnetism and gravity.  This new machine way of thinking may suggest “simple” is wrong.  Google’s AlphaGo program can now beat anyone playing Go…but cannot teach you how to play Go.  It thinks differently than its human competitors.

Dave noted that for thousands of years, we acted as if the simplicity of our models reflected the simplicity of our universe.  We are beginning to learn that with an almost infinite number of interrelated variables, the real world is too complex to “know.”

In Tom Friedman’s book, Thank You For Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations, he suggested that Mother Nature provides a good model for facing the future, as Mother Nature has been adaptable and resilient for billions of years.  Mother Nature has shown the ability to adapt when new knowledge becomes available, the ability to embrace diversity, the ability to balance micro- and macro-level processes, and the ability to change in a sometimes brutal fashion.  In other words, Mother Nature crunches the numbers and does not attempt to simplify the models.

I am not sure why..but this all came to mind as I read Tony Bates’ post “What is online learning: Seeking definition.”  Tony described a new survey going out to all Canadian institutions of higher education, seeking to understand the future direction of elearning in Canada.  He noted that simply defining “online learning” has proved problematic, as different institutions have somewhat arbitrary definitions of online, blended, hybrid, and even the differentiation between credit, contract, and free courses.  Over the past two decades, I have run into similar issues trying to define what is meant by online learning.  I have run the gamut from structured courses run asynchronously (and sometimes synchronously) through LMSs…to “It’s All Frigging Online!” – meaning that we are all now so interconnected that “online” is simply a continuum by which learning can be facilitated…but that continuum rarely approaches zero.

Tony noted that the results will be available in early September, and I suspect his team will learn from these results.  I also wonder if sufficient data already exists in the cloud that machine intelligence could look at the same issues and come to new understandings which might be difficult for us to understand?

It is amazing that we live in an era where contemplating that is even possible!

{Graphic: The Vital Edge}

The Future of Higher Education?

My good friend Enoch Hale asked me a question late last week that I have been contemplating ever since:

“What are some good books to read regarding the future of Higher Education?”

Good question…and at a deeper level, how do you differentiate between books that have the flawed (at least, I think flawed) assumption that higher education tomorrow will resemble higher education of the past…and books that actually suggest a new future?  Search for books on “the future of higher education” and you quickly find quite a few…and I would add in books about “the future” itself.

There are lots of ways to think about the future…

In Tom Friedman’s book, Thank You For Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations,  he draws the distinction between typical course creation processes, which can take a year, and Udacity’s roll out of a MOOC for Google’s TensorFlow program 3 months after Google announced the algorithms.  Friedman sees coming disruptions from intelligent assistants and intelligent algorithms.  Yet, Friedman also points out that in an era when it is possible to automate much of the learning process, the element that marked “successful students” was the human element – teachers and mentors who took personal interest in students.

One way to think about the future of higher education is to think about the future into which our students will graduate.  In “3 graphs that explain how higher ed needs to design for the future of work,” Education Design Lab noted that:

  • Job hopping will become the norm
  • Most jobs will require post-secondary education
  • Jobs are either very susceptible or fairly immune to computerization-with little middle ground

New York Times discussed a higher education leaders forum last year that suggested the key challenges for higher education included finding new ways to teach the digital generation, bringing down the cost of a college education, and ensuring more students graduate.  A recent Harvard graduate has been exploring micro-financing and vocational education as one approach outside the United States…and one wonders if some in this country might take this route as well.

Forbes carried an article this past year that suggested that return on investment is the biggest issue facing higher education…with good reason.

I remain an optimist.  I like the direction(s) Kevin Kelly’s The Inevitable takes, in which new technological forces will drive new opportunities.

I also like the direction Stanford has in their 2025 strategic plan:

Soooo…if Enoch asked you the question, how would you respond? What should we be reading to inform our vision of higher education’s future?

 

Annual Reflection on My Tool Use

carpenter-tablet-computer-manual-worker-hammer-toolbeltJane Hart has opened up voting for the 2017 Top Tools used for learning.  With the 11th Annual Learning Tools survey, Jane Hart will once again be compiling an overall Top 200 Tools for Learning 2017 as well as 3 sub-lists:

  1. Top 100 Tools for Personal & Professional Learning 2017 – ie. the tools used by individuals for their own self-organised learning and self-improvement – inside and outside the workplace.
  2. Top 100 Tools for Workplace Learning 2017 – ie. the tools used to create and/or manage e-learning or for performance support, or tools used by work teams and groups for informal social and collaborative learning.
  3. Top 100 Tools for Education  2017– ie. the tools used by educators and academics in schools, colleges, universities, adult education etc.

Voting closes: mid-day GMT, Friday 22 September 2017
Results released: 8 am GMT, Monday 2 October 2017

I frequently use her annual Top Tools for Learning in both my doctorate and masters courses.  My look at my use of tools and my Top Ten were posted last September.

So my Top Ten this year are:

  • Twitter
  • Tweetdeck
  • Diigo
  • Feedly
  • Netvibes
  • Camtasia
  • SnagIt
  • WordPress
  • Facebook
  • Apple Watch

Not much has changed in the past 7 months…though I changed out my number ten:

Some of the shift over the past three years comes as I retired from full-time faculty development and spend more time in online teaching.  However, I still dabble in faculty development – I have just spent the past two months consulting for the VCU School of Social Work as they update their elearning offerings.

I teach for both Northeastern University and Creighton University.  That means two different LMSs (Blackboard and Canvas), but the LMS does not make my top ten…and I continue to be comfortable teaching in any (or none).  I introduce my students to blogging and social media, so Twitter, Tweetdeck, Diigo, WordPress, and Facebook are all actively used in my instruction (and in work submitted by my students).  I personally use Tweetdeck, Feedly and Netvibes to organize student tweets and blogs.  Camtasia and Snagit are used frequently to create multimedia for my classes…or respond to student questions.  I also instruct my students on curating their own content, and a favorite of my students this past year has been Pinterest.

I started using the Apple Watch this year..and it is amazing how quickly that becomes a part of daily use, from seeing social media to texts to fitness apps…and the timer keeps me on time to meetings!  So it seemed right to add it to my top tools, even though I continue to use the iPhone, iPad, and laptop daily…as well as my trusty Dell desktop.

I poll my students frequently to see what they are using…and some surprises show up (at least for me):

Big shout out to Jane for continuing this interesting snapshot of tool use across corporate and education settings!  I look forward to seeing this year’s list…and I hope to spend some time this summer exploring some of the emerging tools that showed up last year.

{Graphic: Dreamstime}

Value Added versus Liability Sponge

Someone who always gets me thinking is danah boyd.  Her post “Toward Accountability: Data, Fairness, Algorithms, Consequences” is the latest to prod my brain!

liability spongeHer post raises the issue of how data collection and data manipulation are not neutral activities…that the decision to collect or not collect and the thought process behind the analysis of data have value implications.  An example she used was around open data and how the transparency of data about segregation in NY schools led many to self-segregate, leading to more segregation, not less.  In another example, she noted how Google’s search algorithms picked up racist biases by learning from the inherently biased search practices of people in this country.

danah noted toward the end of her post:

“But placing blame is not actually the same as accountability. Researcher Madeleine Elish was investigating the history of autopilot in aviation when she uncovered intense debates about the role of the human pilot in autonomous systems. Not unlike what we hear today, there was tremendous pressure to keep pilots in the cockpit “in case of emergency.” The idea was that, even as planes shifted from being primarily operated by pilots to primarily operated by computers, it was essential that pilots could step in last minute if something went wrong with the computer systems.

Although this was seen as a nod to human skill, what Madeleine saw unfold over time looked quite different. Pilots shifted from being skilled operators to being liability sponges. They were blamed when things went wrong and they failed to step in appropriately. Because they rarely flew, pilots’ skills atrophied on the job, undermining their capabilities at a time when they were increasingly being held accountable. Because of this, Madeleine and a group of colleagues realized that the contexts in which humans are kept in the loop of autonomous systems can be described as “moral crumple zones,” sites of liability in which the human is squashed when the sociotechnical systems go wrong.”

These two paragraphs seem to provide some context to the chapter I am currently reading in Tom Friedman’s new book, Thank You For Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations.  In “Turning AI into IA”, Friedman suggested that a part of the solution to dealing with the triple accelerations of technological change, global market change, and environmental change, lies in leveraging artificial intelligence into intelligent assistance, intelligent assistants, and intelligent algorithms.  Friedman noted that in this age of accelerations, we need to rethink three key social contracts – those between workers and employees, students and educational institutions, and citizens and governments.

I totally agree that the status quo is not the answer, whether we are talking corporate structures, higher education, or government.  I worry though the extent to which some would push technology as an answer to higher education.

I firmly believe that integrating digital technology into teaching and learning makes sense…if one starts with the learning outcomes first and chooses the technology for the right reasons.  TPACK still resonates with me!  Smart technology could easily take the place of repetitive practice work, freeing faculty to focus on the underlying critical thinking skills that students must develop in order to succeed in tomorrow’s world.  My worry would be faculty that see the opportunity to place their courses on autopilot while they pursue their research interests.  Like the pilots above, teaching skills could atrophy…setting faculty up as liability sponges if students fail.

Friedman made an interesting observation – that when ATM’s became common in banks, there was an assumption that they would replace bank tellers.  Instead, by reducing the cost of operation, ATM’s made it possible to open many more branches…and the number of tellers increased.  They no longer handled as much cash, but they became instead points of contact with customers.

If one visualizes the higher ed equivalent of an ATM, one might see a future for higher education that involves lower cost, more locations, and more faculty….but faculty “teaching” in new ways.  Now is the time to have those conversations about the future of higher ed, the future of faculty, and the future of learning.  We need to be proactive before we find ourselves in a moral crumple zone of our own making.

{Graphic: Mishra & Koehler}

Got My Attention

Over the weekend, I continued reading Tom Friedman’s new book, Thank You For Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations.  I have to admit that the second section on accelerations – technologies, globalizations, and ecologies – scared the crap out of me!

In a methodical manner, Friedman laid out his case that we as a planet have reached a tipping point.  Moore’s law has reached the point where connectivity worldwide is basically fast, free, easy for you and ubiquitous and handling complexity has at the same time become fast, free, easy for you and invisible due to the cloud.

In his 2005 book The World is Flat, Friedman was widely quoted for stating:

.

In this book, he discusses a global cloud based company that originated in the eastern part of Turkey – not China or India.  In the 12 years since The World is Flat was published, we have gone from competition residing in big countries to competition residing anywhere.  The market economy has shifted from one based on products to one based on flows, which harkens back to the Goodwin quote from a couple of posts back:

“Uber, the world’s largest taxi company, owns no vehicles.  Facebook, the world’s most popular media owner, creates no content.  Alibaba, the most valuable retailer, has not inventory.  And Airbnb, the world’s largest accommodation provider, owns no real estate.  Something interesting is happening.”

Probably most unsettling is what he called the black elephants – a cross between black swans (low probability events with major implications) and elephants in the room (problems visible that no one wants to acknowledge).   Friedman then went on to discuss in detail nine such black elephants:

  • Climate change (we have already tipped beyond the recommended 350ppm for CO2 in the atmosphere – so we are getting hotter)
  • Biodiversity (the annual loss of species)
  • Deforestation (down to around 62%, where 75% is the level needed to maintain healthy atmosphere)
  • Biogeochemical flows (the addition of chemicals to the water system)
  • Ocean acidification (growing but still within safe limits)
  • Freshwater use (growing but still within safe limits)
  • Atmospheric aerosol loading
  • Introduction of novel new entities (nuclear, plastics)
  • Atmospheric ozone layer (the only limit we as a planet addressed and have moved back from the brink)

Compounding all of these is the continuing growth in human population.  Looking at humanity as a whole, we have increased life expectancy and dropped mortality rates, but not decreased birth rates.  When one looks at all of the black elephants noted above, and then adds the compounding element of adding even more humans to the mix, it paints a dire picture!

So Friedman got my attention…now I need to read the next section to see just where the “optimist” in his title comes in!

Balancing Optimism with Pragmatism

Audrey Watters this week posted a talk she gave at Coventry University earlier this year entitled “The Top Ed-Tech Trends (Aren’t ‘Tech’).”  Good talk by someone I like to follow in my feeds…primarily because she is the contrary voice I sometimes need to hear.  Now I am trying to balance the optimism of Friedman (and me) with the pragmatism of Audrey.

Since 2010, Audrey has published a series of articles covering the trends of the past year in educational technology – a huge undertaking!  She summarized her flow of trends in her talk.

trends2010-2011

trends2012-2013

trends2014-2015

trends2016

Audrey offers her yearly well-researched articles as a counter to the short bulletted list of “must have new cool” technologies that seem to roll out every December and January.  As her list illustrates, her trends are more ideological than technological…which in some ways aligns with Tom Friedman’s mega-trends of simultaneous accelerations of technological change, market change, and climate change.  In both cases, as Audrey noted so well:

“They’re not “trends,” really.  They’re themes. They’re categories. They’re narratives.”

…and as she noted, they are US-centric and even California-centric.  She discussed the narrative flowing out of Silicon Valley…the “dream factory” of California.  This narrative supports an optimism for science as the solution for all the world’s problems.  The focus on skills, personalization, learning to code, disruption … all flow from the California Ideology as described by Watters.  Audrey noted that she chose “the platforming of education” in 2012…and wondered if 2016 saw the failure to platform emerge as a theme.  An interesting observation, as I recall the 2012 optimism associated with A Domain of One’s Own and personalized platforms as the vehicle to lifelong learning.

In many ways, Friedman shared this optimism when he noted that we had entered a world in which”…connectivity was fast, free, easy for you and ubiquitous and handling complexity became fast, free, easy for you and invisible.”  Any problem could now be solved through the combination of fast, free connectivity and fast, free crunching of data in the cloud.  And that is probably true for technological problems.  Within the agriculture economy, the decline in immigrant field workers will probably be solved with automated field workers.  Friedman noted that we have reached an age where you only have to dream about a solution and you can achieve it.  You can build the platform to make it happen.  But Audrey closed her talk by noting that platforms are not substitutes for communities.

Both Friedman in Thank You For Being Late and Kevin Kelly in The Inevitable make an optimistic case that as technology displaces workers, it also creates new jobs requiring new skills. But Friedman also noted that when the Industrial Age displaced the Agricultural Age, it took about a generation for old ways to die off and new ways to surface.  While the rate of change has accelerated since 2007, will our rate of adaptation – both as individuals and as educational systems – match that change rate?  Audrey quoted Neil Selwyn, who identified three contemporary ideologies intertwined with the technological ones – libertarianism, neoliberalism, adnd the ideology of the new economy…to which she added a fourth – technological solutionism.  These four align with concepts of venture capitalism, the gig economy, the shared economy, the attention economy…all happening fast, free, easy for you, and accelerating.

To riff off of the video below, have we become so enamored with personalization that we have lost sight of the person?  One of my students shared this video by Prince Ea with the class…and it is worth a listen.

It is another way of saying…balance optimism with pragmatism…

{Graphics: Watters}

An Accelerating Future


Over the past couple of weeks, I have been exploring the 2017 Deloitte Global Human Capital Trends report, which looked at the challenges ahead for businesses and HR professionals.  The report is based on analysis of a survey of more than 10,400 business and HR leaders globally, and noted ten trends.  Over a series of posts, I have been looking at this report from a faculty development perspective, but folding in thoughts generated from reading Tom Friedman’s new book, Thank You For Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations.

The last two trends involved Diversity/Inclusion and the Future of Work, which again tie in nicely to the accelerating technological changes of the past decade noted in Friedman’s book.  Taking them in reverse order, the report noted that the future of work is being driven by the acceleration of connectivity and cognitive technology.  Friedman noted that between 2000 and 2007, we had a phase shift where “…connectivity was fast, free, easy for you and ubiquitous and handling complexity became fast, free, easy for you and invisible.”

Think about that statement.  When the world went flat, all corners of the world were connected and connected with high bandwidth.  At the same time…and due in large part to those connections and the continued advances of Moore’s Law, machine learning made the potential for handling complexity effortless.  With the huge data available on the cloud, Google can now translate English into any language (and vice versa)…not by programming grammar…but by letting the program compare examples of translated text and look for statistical patterns.  Friedman pointed out that when Google got rid of linguists and brought in statisticians, accuracy of language translation went up.  Now the same thing is happening with speech recognition, and we are approaching the old Star Trek standard of a universal translator.

This use of the cloud has allowed for some amazing transformations in what is “normal.”  Friedman quoted Tom Goodwin in a TechCrunch article as stating:

“Uber, the world’s largest taxi company, owns no vehicles.  Facebook, the world’s most popular media owner, creates no content.  Alibaba, the most valuable retailer, has not inventory.  And Airbnb, the world’s largest accommodation provider, owns no real estate.  Something interesting is happening.”

Now, conceptualize how that “something interesting” will play out in higher education.

The Deloitte report noted that automation, cognitive computing, and crowdsourcing are paradigm-shifting forces that will reshape the workforce.  With AI impacting almost every field, every field will have to identify those “essential human skills” that will differentiate their business and make them competitive.  This obviously will also impact what higher education is doing to prepare the workforce of the future…which in turn impacts what faculty need to do.  The report suggested that the essentially human parts of work – empathy, communication, persuasion, personal service, problem solving, and strategic decision making – are becoming more important, which raises the importance of a diverse workforce.  The report noted that when one considers organizations as networks, it becomes clear that diversity and inclusion can enhance organizational performance.  And diversity is not just gender or ethnic considerations, but diversity of thought as well.

Now consider faculty development in this accelerating future.

The gold standard regarding faculty used to be tenure-track processes.  But in an accelerating future, tenure is simply a waypoint towards an undefined future.  The half-life of the skills and expertise one brings in to tenure will erode rapidly.  More importantly, thanks to cognitive computing, some aspects of “teaching, research and service” could easily be automated.  This is not bad.  Friedman points out that the future will involve teaming of humans with machines.  Rather than a TA, we might have Siri or Alexa or some other cognitive device to help us … and learn with us.

That suggests that faculty – and faculty developers – should be asking:

  • What parts of teaching, research, and service can be automated, and what parts do faculty provide added value?
  • How do faculty reskill … and help students reskill as technology evolves?
  • What learning needs to take place in a classroom and with students physically present and what could be done online?  Synchronous, asynchronous, small group, simulations…
  • What new learning can be (or should be) crowdsourced?  What does this mean for curriculum design?
  • With all this change, time becomes a precious commodity.  How do we redesign faculty (and student) work to be open, collaborative, digital…and yet leave time for exploration and discovery?
  • Will new roles emerge beyond tenure-track, term, and adjunct faculty?  How will faculty development evolve to meet these new roles?  With the world moving to more personalized experiences, will we now have personalized faculty development?

No easy answers…but complacency could be our biggest barrier.  We have to assume that the faculty development model of the past will not fit an accelerating future.

{Graphics: Deloitte Press}