Serendipity35

Albert Einstein was definitely a subject matter expert, but he is not regarded as a good professor. Einstein first taught at the University of Bern but did not attract students, and when he pursued a position at the Swiss Federal Institute of Technology in Zurich, the president raised concerns about his lackluster teaching skills. Biographer Walter Isaacson summarized, “Einstein was never an inspired teacher, and his lectures tended to be regarded as disorganized.” It's a bit unfair to say that "Einstein Was Not Qualified To Teach High-School Physics" - though by today's standards he would not be considered qualified. It probably is fair to say that "Although it’s often said that those who can’t do teach, the reality is that the best doers are often the worst teachers."

Beth McMurtrie wrote a piece in The Chronicle called "What Would Bring the Science of Learning Into the Classroom?" and her overall question was: Why doesn't the scholarship on teaching have as much impact as it could have in higher education classroom practices?

It is not the first article to show and question why higher education appears not to value teaching as much as it could or should. Is it that quality instruction isn't valued as much in higher education as it is in the lower grades? Other articles show that colleges and most faculty believe the quality of instruction is a reason why students select a school.

Having moved from several decades in K-12 teaching to higher education, I noticed a number of things related to this topic. First of all, K-12 teachers were likely to have had at least a minor as undergraduates in education and would have taken courses in pedagogy. For licensing in all states, there are requirements to do "practice" or "student teaching" with monitoring and guidance from education professors and cooperating teachers in the schools.

When I moved from K-12 to higher education at NJIT in 2001, I was told that one reason I was hired to head the instructional technology department was that I had a background in pedagogy and had been running professional development workshops for teachers. It was seen as a gap in the university's offerings. The Chronicle article also points to "professional development focused on becoming a better teacher, from graduate school onward, is rarely built into the job."

As I developed a series of workshops for faculty on using technology, I also developed workshops on better teaching methods. I remember being surprised (but shouldn't have been) that professors had never heard of things like Bloom's taxonomy, alternative assessment, and most of the learning science that had been common for the past 30 years.

K-12 teachers generally have required professional development. In higher education, professional development is generally voluntary. I quickly discovered that enticements were necessary to bring in many faculty. We offered free software, hardware, prize drawings and, of course, breakfasts, lunches and lots of coffee. Professional development in higher ed is not likely to count for much when it comes to promotion and tenure track. Research and grants far outweigh teaching, particularly at a science university like NJIT.

But we did eventually fill our workshops. We had a lot of repeat customers. There was no way we could handle the approximately 600 full-time faculty and the almost 300 adjunct instructors, so we tried to bring in "champions" from different colleges and departments who might later get colleagues to attend.

I recall more than one professor who told me that they basically "try to do the thing my best professors did and avoid doing what the bad ones did." It was rare to meet faculty outside of an education department who did any research on teaching. We did find some. We brought in faculty from other schools who were researching things like methods in engineering education. I spent a lot of time creating online courses and improving online instruction since NJIT was an early leader in that area and had been doing "distance education" pre-Internet.

Discipline-based pedagogy was definitely an issue we explored, even offering specialized workshops for departments and programs. Teaching the humanities and teaching the humanities in a STEM-focused university is different. Teaching chemistry online is not the same as teaching a management course online.

Some of the best parts of the workshops were the conversations amongst the heterogeneous faculty groups. We created less formal sessions with names that gathered professors around a topic like grading, plagiarism and academic integrity, applying for grants, writing in the disciplines, and even topics like admissions and recruiting. These were sessions where I and my department often stepped back and instead offered resources to go further after the session ended.

It is not that K-12 educators have mastered teaching, but they are better prepared for the classroom from the perspective of discipline, psychology, pedagogy, and the numbers of students and hours they spend in face-to-face teaching. College faculty are reasonably expected to be subject matter experts and at a higher level of expertise than K-12 teachers who are expected to be excellent teachers. This doesn't mean that K-12 teachers aren't subject matter experts or that professors can't be excellent teachers. But the preparations for teaching in higher and the recognition for teaching excellence aren't balanced in the two worlds.

2021 was the year of the “Great Resignation.” We have been told that it was a year when workers quit their jobs at historic rates. This is an economic trend meaning that employees voluntarily resign from their jobs. Blame has been aimed at the American government for failing to provide necessary worker protections in response to the COVID-19 pandemic. This led to wage stagnation. There was also a rising cost of living. The term was coined in May 2021 by Anthony Klotz, a professor of management at Texas A&M University.

It's now 2022 and unemployment rates have fallen sharply from their pandemic highs. The labor force participation rate - which is the percentage of people in the workforce, or looking for a job - has increased, though not to its pre-pandemic level.

It was thought in 2020 that 2021 with a vaccine would mark the renormalization of the economy, schools, and life in general. But Covid variants wiped out that vision.

It seems counterintuitive, but to economists quitting is usually an expression of optimism. You don't quit a job unless you have the prospect of another, probably better one, or you don't need to work because of a good financial situation. But the quits happened when inflation is looming, and the Omicron variant is dominating.

Some industries are seeing higher rates of quitting. It isn't surprising that leisure, hospitality, and retail are at the top. Those were hit hard by the pandemic. Healthcare is another and certainly many of those workers were just burned out by the pandemic. But the reasons given for quitting include a lack of adequate childcare and personal and family health concerns about Covid. If the pandemic overwhelmed you at your job, you might have decided to quit even without a new prospect in search of better work opportunities, self-employment, or, simply, higher pay.

Derek Thompson wrote in The Atlantic that there are 3 myths about this Great Resignation. One is that it is a new 2021 phenomenon. Is it really more of a cycle we have seen before or that has been moving into place for years and simply accelerated by the pandemic?

For colleges, it wasn't so much a Great Quit as it was a Great No-Show. The newest report I found from the National Student Clearinghouse Research Center (NSCRC) shows that postsecondary enrollment has now fallen 2.6% below last year’s level. Undergraduate enrollment has dropped 3.5% so far this fall, resulting in a total two-year decline of 7.8% since 2019. As with jobs, not all of that decline is because of the pandemic and it too is a trend that was evident before the pandemic. But Covid didn't help the decline.

Add to these one more "Great" that I see talked about - The Great Deflate. This is the idea that rather than our economy being a bubble that will burst, it's a balloon that is deflating. In "The Great Deflate" by M.G. Siegler, he talks about a more gradual trend. Picture that helium balloon floating at the ceiling on your birthday that day by day has been slowly moving down as it deflates. No burst, just a slow, steady fall.

Is there a connection among all these trends? Certainly, the connection is the economy. Perhaps, there won't be a stock market crash or something like the Dot Com bubble burst, but we see stock market drops of 1, 2 or 3% pretty regularly. Those are significant drops.

Since May 2021 when Anthony Klotz coined "The Great Resignation," other terms have emerged including “The Great Reimagination,” “The Great Reset” and “The Great Realization” terms that express the re-examining of work in our lives. But the quitting wave hasn't broken yet and so Klotz has more recently made three not-so-surprising predictions.
The Great Resignation will slow down
Flexible work arrangements will be the norm, not the exception
Remote jobs will become more competitive

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Economists say rapid quitting and hiring will continue in 2022 despite omicron wave

I love the movie Groundhog Day in which Phil wakes up at 6 AM every day to discover that it is February 2 all over again. His days run the same over and over though he tries hard to change it. We see him repeat the day more than 35 times. 

Today is Groundhog Day and what is repeating - for the 5840th time - is Serendipty35. Today is the 16th birthday of this blog. (Hence the "Serendipity16" title for this post.) 

Of course, the blog is not the same every day, but it is here/there every day. My calculator tells me that the blog changes every 2.7 days. In the early years, I was much more ambitious with 3-5 posts per week. Over the years, I started other blogs and left my university job where all this started and now, I try to post here once a week. 

The more you post, the more hits you get. Currently, the site averages about 7000 hits a day, but that number was double that back in the years when there were multiple posts each week. Then again, this is still a "non-profit" production - not that we would object to profits. The "we" is me and Tim Kellers who used to post here too in the first years but is now keeping the gears turning in the background. 

And Serendipity35 keeps rolling on... 
 

I really enjoyed the Spike Jonze 2013 movie Her, in which the male protagonist, Theodore, falls in love with his AI operating system. He considers her - Samantha - to be his lover. It turns out that Samantha is promiscuous and actually has hundreds of simultaneous human lovers. She cheats on all of them. “I’ve never loved anyone the way I love you,” Theodore tells Samantha. “Me too,” she replies, “Now I know how.”    

AI sentience has long been a part of science-fiction. It's not new to films either. Metropolis considered this back in 1927.  The possibility of AI love for a human or human for an AI is newer. We never see Samantha, but in the 2014 film, Ex Machina, the AI has a body. Ava is introduced to a programmer, Caleb, who is invited by his boss to administer the Turing test to "her." How close is he to being human? Can she pass as a woman? She is an intelligent humanoid robot. She is a gynoid, a feminine humanoid robot, and they are emerging in real-life robot design.

As soon as the modern age of personal computers began in the 20th century, there were computer games. Many traditional board and card games such as checkers, chess, solitaire, and poker, became popular software. Windows included solitaire and other games as part of the package. But they were dumb, fixed games. you could get better at playing them, but their intelligence was fixed.

It didn't take long for there to be some competition between humans and computers. I played chess against the computer and could set the level of the computer player so that it was below my level and I could beat it, or I could raise its ability so that I was challenged to learn. Those experiences did not lead the computer to learn how to play better. Its knowledge base was fixed in the software, so a top chess player could beat the computer. Then came artificial intelligence and machine learning.

Jumping ahead to AI, early programs were using deep neural networks. A simplified definition is that it is a network of hardware and software that mimics the web of neurons in the human brain. Neural networks are still used. Neural network business applications are used in eCommerce, finance, healthcare, security and logistics. It underpins online services inside places like Google and Facebook and Twitter. Give enough photos of cars into a neural network and it can recognize a car. It can help identify faces in photos and recognize commands spoken into smartphones. Give it enough human dialogue and it can carry on a reasonable conversation. Give it millions of moves from expert players and it can learn to play Chess or Go very well.

Alan Turing published a program on paper in 1951 that was capable of playing a full game of chess. The 1980s world champion Garry Kasparov predicted that AI chess engines could never reach a point where they could defeat top-level grandmasters. He was right - for a short time. He beat IBM’s Deep Blue in a match over six games with 4:2 just as he had beaten its predecessor, IBM’s computer Deep Thought, in 1989. But Deep Blue did beat him in a rematch and now the AI chess engines can defeat a master every time.

A more challenging challenge for these game engines was the complex and ancient game of Go. I tried learning this game and was defeated by myself. Go is supposed to have more possible configurations for pieces than atoms in the observable universe.

Google unveiled AlphaGo and then using an AI technology called reinforcement learning, they set up countless matches in which somewhat different versions of AlphaGo played each other. It learned to discover new strategies for itself, by playing millions of games between its neural networks, against themselves.

First, computers learned by playing humans, but we have entered an even more powerful - and some would say frightening - phase. Now beyond taking in human-to-human matches and playing humans, the machines tired of human play. Of course, computers don't get tired, but the AIs could now come up with completely new ways to win. I have seen descriptions of unusual strategies AI will use against a human.

One strategy in a battle game was to put all its players in a hidden corner and then sit back and watch the others battle it out until they were in the majority or alone. In a soccer game, it kicked the virtual ball millions of times, each time only a millimeter further down the pitch and so was able to get a maximum number of “completed passes” points. It cheated. Like Samantha, the sexy OS in the movie.

In 2016, the Google-owned AI company DeepMind defeated a Go master four matches to one with its AlphaGo system. It shocked Go players who thought it wasn't possible. It shouldn't have shocked them since a game with so many possibilities for strategy is better suited to an AI brain than a human brain.

In one game, AlphaGo made a move that was either stupid or a mistake. No human would make such a move. And that is why it worked. It was totally unexpected. In a later game, the human player made a move that no machine would ever expect. This "hand of God” move baffled the AI program and allowed that one win. That is the only human win over AlphaGo in tournament settings.

AlphaGoZero, a more advanced version, came into being in 2017. One former Go champion who had played DeepMind retired after declaring AI "invincible."

One of the fears about AI is when it is embedded into an android. Rather than find AI in human form more comforting, many people find it more frightening. Androids (or humanoid robots, gynoids ) with strong visual human-likeness have been built. Actroid and Repliee Q2 (shown on this page) are just two examples that have been developed in the 21st century. They are modeled after an average young woman of Japanese descent. These machines are similar to those imagined in science fiction. They mimic lifelike functions such as blinking, speaking, and breathing and Repliee models are interactive and can recognize and process speech and respond.

That fear was the basis for Westworld, the science fiction-thriller film in 1973 film and that fear emerges more ominously in the Westworld series based on the original film that debuted on HBO in 2016. The technologically advanced wild-West-themed amusement park populated by androids that were made to serve and be dominated by human visitors is turned around when the androids malfunction (1973) and take on sentience (series) and begin killing the human visitors in order to gain their freedom and establish their own world.

Artificial intelligence (AI) in a box or in a human form now plays games with others of its kind. Moving far beyond board games like chess and Go, they are starting to play mind games with us.