Futuremark

Something I tweeted earlier this week…but it keeps circling around in my head:

Harold noted in “the uncertain future of training” that training courses are artifacts of the past…when resources (and information) was scarce and connections were few.  Training courses efficiently collected people together to deliver the training…but that training always looked backwards to “how things were done.”   Shampoo, rinse, repeat…

As Harold noted:

“…Training looks at how people currently do work and then gets others to replicate this. These are described as competencies, made up of certain, skills, knowledge, and attitudes. The assumption was that what works today will work tomorrow. The training department assumed the status quo…”

Yet, we do not live in a status quo world…as Tom Friedman noted in his book, Thank You For Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations, it is a world driven by the acceleration of connectivity and cognitive technology.  Tomorrow will not look like today.

Harold provided a graphic that he has used several times…but it nicely captures this shift:

Work learning shift

So in thinking about training, Harold noted that “the notion of best practices still permeates the business of training. By looking at what is currently being done well we can replicate this and pass it on through training. Best practices, and even good practices, assume a state of order.”

In reading this, I thought of a presentation Tom Peters did in Atlanta over a dozen years  ago…that still resonates with me.  Around slide 282 (out of over 400) in his tenth chapter of The Works Powerpoint, he showed:

Future Mark

Don’t Benchmark…Future mark!  Peters goes on a few slides later to suggest:

  • “Benchmarking Rule #1: “Best practices” are to be learned from, NOT mimicked/treated as law. “Best practices” must ALWAYS be adapted to local conditions!
  • Benchmarking Rule #2: When pursuing “best practices,” DON’T benchmark. FUTUREMARK. Tomorrow’s stars are already out there. Find ’em!
  • Benchmarking Rule #3: DON’T benchmark. OTHERMARK. E.g., a tech company  can adopt “WOW” service practice from, say, a local restaurant or car dealer.
  • Benchmarking Rule #4: Make benchmarking EVERYONE’s biz. Everyone collect best “everyday life” practices. Share WEEKLY.”

A dozen years ago…yet Peters was already seeing what Jarche and Friedman now see.  Couple Peters’ four rules with social media, and you actually have a vehicle that makes “futuremarking” possible.

Soooo…as you put together your summer faculty development bootcamps and institutes, is the focus on best practices or futuremarks?

{Graphic: Jarche.com, Tom Peters}

 

Will AI Do Improv?

There has been a lot on the news lately about artificial intelligence and how it is impacting the future.  Already there are advice posts regarding how AI can enhance education, such as “7 Ways Artificial Intelligence Will Change Higher Education” or “Could Online Tutors and Artificial Intelligence be the Future of Teaching?”.

Yet, this morning as I was driving and listening to Fred Childs on NPR, something his guest said resonated with me.  A pianist noted that even though songs have very clear “rules” in the form of sheet music, whenever he plays a piece, there is improv, because how he plays depends on who he is playing with and what the mood of the audience is.

This idea of improv reminded me of an unexpected flow on Twitter this week in my Northeastern University class on Social Media at #EDU6333.  The current class is a little different than earlier classes I have taught, in that – feeding off each other – they love to add GIFs to their tweets.  Whereas this might have happened infrequently in past classes, it happens every day in the current class.  And I would suggest “feeding off each other” is simply another definition for improv.

Wednesday night, I was grading papers and watching the hashtag feed when the following began happening (I added a few earlier ones, but most posted between 7:45-9:15pm):

Granted, this was only a fifth of the students in the class…though others the next day lamented missing the exchange.  But this is engagement…and dare I say it

It reminds me of the improv associated with teaching…whether K-12 or higher education.  This week we explored constructivist learning and TPACK…yet the dialogue on Twitter went in lots of directions.

We are not at the point yet where AI is a threat to replacing teaching.  After all, scientist last year made a teen robot…and it got depressed.  We have not yet reached the point where machines can empathize with us…though in some ways they are now thinking in ways we no longer understandWith the massive data crunching afforded by the cloud, artificial intelligence may develop new ways to improv in the future.  If anything, we are approaching the time when it will be a natural enhancement to good teaching.  But just as good pianists improv when playing a standard set of sheet music, both teachers and students need to improv when learning together…which is what constructivism is all about!

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}

Only Been One Decade

Freidman bookI loved the second chapter of Tom Friedman’s new book, Thank You For Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations.  The chapter title is

“What the Hell Happened in 2007?”

Good question.  It has only been a decade since 2007, and given that I joined the VCU Center for Teaching Excellence in late 2006, it sort of marked my start in faculty development.

Friedman noted how in a short period right around 2007, the following occurred:

  • The iPhone was introduced
  • Facebook opened up to non-college users
  • Google bought YouTube and launched Android
  • Amazon released Kindle
  • Michael Dell returned to Dell to run the company (again)
  • Intel added non-silicon materials to chips, which helped Moore’s Law to continue
  • The beginning of an exponential rise in green energy – solar, wind, and biofuels
  • The cost of DNA sequencing began dropping to rates anyone could use

Friedman noted that he first began writing a book about how technology was driving the world…and the world’s economy… back in 2004, which became The World is Flat.  He updated the book in 2006 and issued version 3.0 in 2007, at which point he stopped thinking about it.  I noted in previous posts that this book was very impactful to me personally.  In fact, my presentation during my interview for a job at the VCU CTE was on how Friedman’s 10 flatteners were changing our view of what it meant to teach.  A version I loaded into Slideshare a year later has now been viewed over 18,000 times, which is just one more example of how the world of teaching has changed!

Yet, in 2010, Friedman picked up his first edition and scanned the index, noticing that Facebook was not in it.  Twitter was not in it.  Big data was not in it.  Skype, LinkedIn, 4G…none showed up in his book about how the internet had changed the world.  That was when he realized the extent to which these changes were indeed accelerating.

As I think back on this last decade and my evolution within the VCU CTE … and later on to Northeastern’s Center for the Advancement of Teaching and Learning through Research (CATLR), I realize how fortunate I have been to have had the opportunity to play at precisely that inflection point in history when our concept about teaching and learning in a digital world changed.  I also got to play in a wonderful team led by Jeffrey Nugent, with Bud Deihl playing alongside.  2007 marked my first year as a learning specialist at the CTE, and during that year, Koehler and Mishra published their first paper on TPACK – Technological, Pedagogical and Content Knowledge, which shaped much of my work with faculty.  We began paying attention to work Stephen Downes and George Siemens were doing around the concept of connectivism, as well as the first MOOCs.  I sent my first tweet …even misspelling it as “twit” … in January 2008.

I also started this blog in January 2008.  Three hundred-seventy-five posts later…here we are…

It has only been one decade!

Friedman ends the second chapter noting that the rate of technological change has increased for the first time above the rate at which humans adapt.  He suggests that we have to now enhance our ability to adapt…which will lead to the next series of chapters.

This need to enhance our adaptability as we deal with the constant acceleration of technology, globalization, and climate change was again on my mind as I continued exploring the 2017 Deloitte Global Human Capital Trends report, which looked at the challenges ahead for businesses and HR professionals. Over the past seven posts, I have been looking at it from a faculty development perspective.  The report is based on analysis of a survey of more than 10,400 business and HR leaders globally, and noted ten trends.  I discussed the fifth trend yesterday.

The sixth and seventh trends involved Digital HR and People Analytics, which are not only closely related…but tie in nicely to the accelerating technological changes of the past decade.

The report noted that HR now is dealing with a digital workforce, a digital workplace, and so must be digital as well.  The tone has shifted from “doing digital” to “being digital.”  Companies are shifting from rigid place-bound organizations to networks of networks.  Processes are expected to be more transparent, and new tools are needed.  “Standard” HR practices are becoming anything but standard as organizations fluidly shift in order to optimize productivity, engagement, teamwork, and career growth.  Analytics are now being mined to help drive performance.

The concept of being digital aligns with faculty development as well.  In a conversation this past week with a colleague, she noted that online teaching is no longer seen as an add-on…that being digital is part of teaching today.  We lag behind corporate America when it comes to using analytics…but that is changing as well.  One only need look at the sales pitches by companies for the various LMSs to see how analytics are now in the lexicon of education.

If change is indeed accelerating, one wonders what the next decade will bring.  I plan to shift the textbook for my Creighton Leadership and Technology course from Dave Weinberger’s Too Big to Know to Kevin Kelly’s The Inevitable, but I can see that by Spring 2018 when I next teach this course, Friedman’s book may also be part of the course.

Maybe that is inevitable…

{Graphics: Deloitte Press}