Sunday, January 15, 2017

So much beauty ... so little ...

So much beauty, so much tragedy, so much nonsense …what shall we make of it all?

From CNN - Myanmar's Mergui Archipelago

Lake Louise in Canada:
My own picture taken in Bali in 2016:

And another  one of LA (Lower Alabama) coast at sunset:

I have been so fortunate to see so much natural beauty in so many different places.

Then I think about the beautiful people I have known – especially my children and grandchildren and their spouses. So many acts of kindness and understanding I have witnessed through them – makes me want to be  more kind and understanding.

I need to have these thoughts of beauty when I think about so many tragedies in so many places, from Aleppo to Haiti and too many other places to mention, many of which are closer to home. Natural tragedies such as hurricanes and earthquakes are beyond our control, but many tragedies are within our spheres of influence, yet they continue to occur, in my own country and elsewhere around the world.

Like Lawrence Ferlinghetti, I am waiting (see – seems it has been a long time coming … and a lot of waiting for that rebirth of wonder … seems we may have to wait another 4 or 8 years or more … but we should do more than wait. Hope is not enough, either. What can one do? One can find another and create a small place in which wonder can be reborn. Or maybe it takes more than two?

Learning Context

What constitutes a learning context and why should we care?

It is a reasonably well-established view that context is in large part what determines the meaning of an utterance. Context – or more specifically, use, which occurs in a context - in part determines meaning (see for an elaboration). The point is that use and context are critical factors in determining the meaning of what someone has said. Likewise, the use and context are critical factors in determining whether, how and why learning might or might not be occurring in a particular situation. So, to determine if meaningful learning is or is not occurring or likely to occur, one needs to consider the learning context and what the learner might be doing or not be doing in that situation.

The simple approach to providing a description of a learning context is to indicate what is around the learner during a learning activity. It might be other learners, a teacher, a book, and a computer as in a typical classroom setting, or it could be music playing in a coffee shop with other people around who may or may not be learners, or it could be many other situations in which learning is intended to occur.

However, simply describing what is around a learner falls short of providing a good or complete understanding of a learning context. What the learner is doing or might do is also relevant. If the learning situation happens to be in a coffee shop with music playing, then a learner is not likely to benefit much from an audio or audio-video file played on that learner’s laptop computer. The coffee shop situation does not provide effective affordance or support for audio-based learning. Likewise, in a classroom setting, if the learner is working with a small group of students who are busy chatting on their smartphones, that context might have too many distractions to support learning to solve a complex problem, unless the chatting happens to be about that problem and is providing some insights about what to do.

The point I am trying to make is quite simple. When describing a learning context, it is reasonable to include all of the things that a learner can touch (or see or hear or smell or taste) as well as all of the things that can touch the learner. In addition, just as the speaker is part of the context when it comes to determining the meaning of what is said, the learner is part of the context when it comes to understanding to what extent (and why) meaningful learning might be occurring.

Of course what is to be learned is part of the learning context as well. What is to be learned includes the general topic, the level of understanding sought, as well as the specific knowledge, skills and attitudes that are part of the learning goal and expected outcomes. 

Tuesday, January 10, 2017

The More Things Are Called Smart …

It used to be the case that there were a few people one might call smart. Now, we call phones smart, whiteboards smart, computer programs smart, learning environments smart, technologies smart, and even automobiles and cities smart. What has become of the meaning of the descriptive adjective ‘smart’?

What I really crave is a smart chocolate ice cream cone. I suppose someone has an answer for that as well … perhaps one that has no sugar, no calories, and no reason to ask for seconds. I am wondering about the meaning of ‘smart’ because I was asked about it in a meeting with faculty and graduate students in the Educational Technology Department in the Institute of Education at Tsinghua University in Beijing.

Perhaps the location of an educational technology program within a university reflects or influences how ‘smart’ is conceptualized. One can find educational technology programs in a college or faculty of education, or in a college or faculty of information, or information science, and even in an arts and sciences college or faculty. In an education college or faculty, one might imagine the emphasis to be on technologies that can be used to make people smarter. In a college of information or information science, one might imagine the emphasis to be on devices and technologies that did things that people who used to be called smart used to do. It would be an interesting exercise for someone to look at where educational technologies were located within a university structure, the various emphasizes and perspectives reflected in those programs, and any patterns or correlations that might emerge from those data.

My own views are shaped by my experience – first with philosophy and later with computer science. My philosophy perspective is based in skepticism (e.g., recognizing the limits of knowledge and the ease with which we are inclined to believe we know more than we actually know or believe that the world happens to conform to the limits of our knowledge) while my computer science perspective is based on artificial intelligence and expert systems. I have seen the word ‘smart’ used quite differently in both of those contexts.

I would consider Socrates smart, for example. He would elicit ideas about such things as justice and virtue from the sophists and show that their beliefs led to contradictions or absurd conclusions, such as a virtuous ruler as one who manages to squeeze the most work and highest taxes from citizens, applying the notion of virtue as that which benefits the stronger – with the conclusion that what is good for one is not necessarily good for another. I was fascinated early in life by the logic of such an indirect proof. I had also seen it used in mathematics, as in the common proof that pi is an irrational number – assume it is rational and then step by step one arrives at a contradiction which means the point of departure was not sound. Pi must be irrational – aren’t we all from time to time?

And then to earn a living, I took up computer science and became fascinated with expert systems and the representation of human expertise in software programs. I was attracted to complexity and the notion that somehow software might be able to extend the ability of people to deal with complexity, which could be conceived as a way of making those persons smarter – able to perform more like experts.

One vivid memory I have of my time as a practicing computer scientist involved being responsible for some 400 plus programs written in Assembly Language and Fortran running simultaneously on five different computers, four were 32-bit Perkin-Elmer mini-computers and the fifth was a 30-bit graphics computer. The application was real-time simulations of air combat training. One task I had was to add software tracking of the sun’s position along with a number of fighter jets that would simulate firing air-to-air missiles at enemy aircraft and record probable hits and misses and then debrief the pilots after the exercise. The task was to show the pilots the location of the sun in their cockpit views when they fired a heat-seeking missile, as it would go after the sun and miss the target. Getting that simple change to work proved a very complex and challenging task. I got stuck trying to make it work as every time I thought I had it figured out, the software would crash (in simulated runs rather than in actual exercises – we always made sure the software would work before using it in live exercises).

After a week of pondering over the crashes and narrowing the logic failure to a few lines of code among the thousands of lines involved, my mentor and a senior programmer (Walt Davis) advised me to make a single random change in those few lines and see what happens. He also could not see a problem with the logic and agreed the problem had to be among those lines of code. The change I settled on was to reload a memory location with the same doubleword that was just loaded in the previous line of code – yes, that was in Assembly Language. Unbelievably, the change solved the problem. What I had thought had to be a problem with the logic proved to be a timing problem. By reloading that register with the same information, the other programs running in parallel had time to catch up and do their tasks. Walt was smart. He did not know what was wrong but he knew I needed to get more information, and making one small change was a way to start getting more information.

Perhaps a smart computer processing system would have suggested a similar change, but I have yet to see such a system perform with the smartness of a Walt Davis. I suppose that might become possible at some point.

Back to my point – what is with all these uses of the word ‘smart’. Is it not hard enough to identify a smart person much less identify which devices are smart or which are only being called smart so as to improve sales. Some leaders and leaders to be call themselves smart. The sophists with whom Socrates interacted called themselves smart. Self-attribution of smartness seems to be a likely indication of lack of smartness.

At least devices are not yet able to call themselves smart. Yes, that will probably happen at some point. But then I recall Bob Gagné’s advice to me while working together at the Air Force Armstrong Laboratory in San Antonio. He said, “our job is to help people learn – to help them become smarter … better at solving problems.” Oh yes. We might even be able to use various devices and techniques in doing that all important job.