Posts Tagged ‘learning’

I spend much of my time these days doing long-term strategic research and planning. Part of that time is spent identifying areas where technology training is warranted. The ways and means I use to create and present training materials have been developed through years of trial and error. In the midst of one particular line of research into a non-training-related area, I found Building an Innovative Learning Organization by Russell Sarder.

The book is relatively short, about 220 pages, but in many ways, you really don’t need more than that to cover the concepts of training. While it’s true that it would take far more to cover all aspect of training, from organization by-in, to facilities, to choice of materials, to length of courses, etc., those are details. And the details are as pointless as ornaments without a tree if you don’t have the fundamentals in place. That’s where this book shines.

Yes, there are all the requisite elements of a business-oriented book (voices from industry, outcomes of research, anecdotes, and the like). Not to mention the mound of acronyms tossed in for good measure. But, I expect those. This book asserts that learning should be a systemic attribute of any thriving company. As such, learning must be part of the culture of the company for it to be successful. You cannot slap training on the side and expect that you will have any serious ROI to the company. It would be like thinking that buying Girl Scout cookies or Boy Scout popcorn has a substantive impact on the members of either organization. Yes, it does provide financial support for programs, but it’s not “the program.”

Training needs leaders, resources, people interested in learning, and a purpose (lest we forget why we do training in the first place).

Training has a structure and that structure is not one-size-fits-all. People have varying modalities of learning. Even the best material won’t work well for everyone. This is were that whole (materials, time, place, etc.) details thing comes into play. But, again the focus of the book is to lay out the challenges and considerations, not specifics.

Finally, you need to see that training produces results. This can be fiendishly difficult to measure, so it’s vitally important to set expectations before doing the training. Being happy is not considered a valid measure of ROI for the company.

As mentioned earlier, the book is replete with references and for those who create training material or even those who want to create an environment within their company where can be effective. It is a good starting point. For those who have been involved in training for some time, the book can serve as a reference that can be used to educate management in the scope, cost and investment (they’re different) necessary to create a learning environment that will have long-term benefits.

Overall, a decent read. I found the interviews with CLOs (chief learning officers) incisive. As with all organization-level things, there are no easy answers. And you do get what you pay for. You’ll dispatch this book in a few hours and then find yourself going back over it later.


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I’ve been reading Isaacson’s DaVinci biography (that’s another post) and thinking about metaphors, analogies, teaching and learning.

Teaching is hard. The world is a complex place, so that’s to be expected. Learning is hard, although many people expect it to be easy. I mean, really, like, you can just Google things.

Well, really, not so much.

For me teaching is all about the group and the motivating example. Humans learn best by metaphor, going from the known to the unknown. Kind of like having one foot firmly planted on the lip of the hot tub and testing the temperature with the other. Just jumping in might work. Not something to rely on though. If you give people a framework they can relate to, it affords them a place from which to extend what they know.

On my high school senior physics class final was a problem that began, “A rock explodes into three pieces …”. Really? Why? It’s been a lifetime since that event and yet the premise of the problem still sticks with me. During my undergraduate studies, I had a physics professor whose motivating examples were based on James Bond situations. As contrived as physics problems tend to be in order to tease out a self-contained use of some specialized equation, at least contextualizing thing via James Bond gave them a veneer of reason. Mostly. Sort of.

During my graduate studies, I dropped a class in neural networks because the professor presented the material in such an abstract fashion that I couldn’t anchor it. It wasn’t until I took Andrew Ng’s first Machine Learning class on Coursera (which was one of two first offered) that neural networks actually made sense to me. He presented the material in the context of real-world use cases.

I’m not say that everything can be learned by simply having a good story. If you work with computer software long enough, you’ll have to confront numbers represented in binary, octal or hexadecimal. You’ll just have to memorize the conversions. The same is true for operator precedence.

Let’s look at learning for a minute, lest everyone think that I’ve forgotten it.

In order to learn something, assuming that it’s not rote memorization, you must accept the framework within which it exists. Unless you put can do that, things won’t stick. You will forever be condemned to Google it hell. I can usually tell the people who will have difficulty learning a programming language when they complain that it’s not like the language they’re used to. As I like to say, “you can program C in any language.” Some people never get past that point. And we all suffer because of it.

I’m not limiting this to C-based languages. The interpreted world has more than their fair share of people still programming BASIC in any language. I like to think of them as the Python without classes crowd. I’m not sure where the whole “classes are bad” mentality came from, but it seems to have a strong following.

For a less software example, consider using a word processor. Do you still type two spaces after the period? Unless you’re using a typewriter, all you’re doing is messing up the formatting software (technically hyphenation and justification (H&J) system). Try this experiment. Take a word processing document and look at how it formats the text with both a single and double space. This becomes especially evident when full-justifying paragraphs.

All well and wonderful, but what about the pretzels?

Yeah, about those. I struck me that this whole teaching / learning thing can be likened to making pretzels. You know the big, soft, knotted, salt-covered ones. Consider the dough as the learner, the salt and shape the material to be learned and the kitchen equipment the methodology. The cook is the teacher. If the dough is frozen or dried out, it can’t be shaped. This is a refusal to accept the rules of the material. If the equipment is inadequate or the cook lacks an understanding of how to use it, the results will be inconsistent. Likewise, if the cook doesn’t understand how to handle the dough or when to apply the salt, things will probably not be the best. It is only when all three elements are brought together properly that the expected outcome is achieved consistently.

In the realm of teaching this means that the teacher needs to be able to create a motivating example and framework that works for the learners. This changes over time. Just as the world changes. The teacher should be always looking for signs that a student is frozen and be ready with additional material they may more readily relate to. The most difficult cases are the dried out students. They see no need to learn the new material and are at best taking up air. At worst, they are disruptive. These individuals should be given to understand that their presence is optional and that others should be allowed to learn.

Finally, as a teacher, always, always be looking for what you can learn from the students. The world is bigger than you little pretzel shop.

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When I was asked to create and teach a Python class, I had to ask myself, “where is the starting point for this language?”

When it comes to computer languages, I like them logical, powerful, compact and fast. The language currently at the top of my list is Swift. When it comes to longevity, C++ wins hands down. Python is neither compact nor fast. It is, however, very popular. It’s also very flexible.

The C language has so many children that it’s easy to use analogs. What about Python?

I’ve taken intro CS courses from Harvard, Rice and Stanford all of which use Python. They all teach C programming in Python in my opinion. I get where they’re coming from. You’ve spent years using FORTRAN then Pascal then Java.

Happily, my first language was FORTRAN. You think you need to build all your own data structures if you use C. Consider yourself lucky. But that’s a story for another day. My second lanugage as APL. Go ahead try to teach APL the way you teach C. Knock yourself out. A bit later I picked up BASIC, which after FORTRAN was trivial. Next came Forth. That took a bit to wrap my procedural head around. My experience with APL had taught me that it’s perfectly fine to focus on the data and not the process. Forth’s focus on the stack is strangely intriguing. The fact that it lives on in UEFI and Postscript is a testament to the fact that there is value in that view of the world.

So my approach was to start with data representation. In Python the world is all objects and references. But for some reason, people don’t want to approach the language from that standpoint. They like to talk about how easy it is to write ‘hello, world’ programs or how it’s more readable than Perl. Aside from APL, I don’t know anything that’s less readable than Perl. Except maybe TECO macros.

Now that I had a place to start, life should be a hop, skip and a jump to classes, yes? Not so fast pilgrim. It’s easy to explain that everything is an object and that integers are a sub-class of rationals. It’s easy to explain that 0 takes 12 bytes of storage. You can even justify the lack of a character object. But I think you do a true disservice if you don’t address the fact that strings are Unicode based. I’ve dealt with enough internationalization issues to know that to gloss over this would be a disservice to the student. I probably spent more time working on the string section of my class than any other.

The reason? It’s one thing to present information that raises obvious questions like, “so you’ve told me that there as multiple ways to represent the same grapheme and that these strings will have different code units, but what am I supposed to do about it?” Or “how am I supposed to sleep knowing that a Vai 4 digit isn’t the same as an Arabic numeral 4?” You might as well throw them under a bus if you honestly believe that you’ve discharged your duty as a teacher by telling the students that there are land mines out there and that they should bring an umbrella. You’ve essentially just given them a compelling reason to never use the language.

Once all the ‘core’ data types are out of the way, it’s time for some core data structure like list, tuple, and namespace.

Functions, generators, and lambdas come next. These are relatively straightforward. The trick to generators is to show the equivalent implementation in C++. Yes, they are different beasts, but you can get close enough. Similarly with lambdas.

Now, you’re in a position where classes can be reasonably explained. A point to mention here is that back before embarking on a exposition of data types, keywords and variable conventions were addressed. Now, for most students, this goes in one ear and out the other. Having arrived at classes, all those naming conventions come back like an overeager Sheltie wanting to play. Ignore them at your peril. Enumerations are also introduced here.

For many, all the object bits will now come into focus. This is a good thing. I’ve never liked the approach where students are taught how to use bits and pieces of libraries without the foundation to understand what’s actually happening. They end up with a false sense of accomplishment and may never seek to build an accurate model of the languages world. They’re like Jeff Goldblum’s character in ‘The Fly’ having no clue how things actually work since he just specified what he wanted a given part to do and plugged the parts together. We all now how that worked out for him. It also emphasizes their importance of debugging you system.

I’ve never understood why some people shy away from classes in Python. They act as though organization is an inherently evil thing. They also probably have all their laundry in two piles in the middle of their bedroom (one dirty, one clean).

Classes point us to resource management, but we can’t do that discussion until input / output is covered. That gives us the idea of using classes (file streams) and their methods to process data. Here’s where string formatting and core data conversion comes in.

Up until now, exceptions have been alluded to. Now there’s enough structure to not only address, but give meaningful examples of their use.

At this point, you’re done with the core language. So we’ll address unit testing. We’ve done bits of this along the way since the introduction of classes, but now we can talk about the unit test library.

What remains are sections on sequence and associative containers. An important aspect of this is teaching how to select the appropriate container. Yes, you can use list and tuple alone, but there are better things to do with your life than reinventing the wheel. Technical interviews insisting that people be able to balance binary trees notwithstanding.

Finally, a brief introduction to the standard library. Before Googling, how about being aware what’s already in the box.

You’ll note a distinct absence of web browsers, GUI applications, client-server systems, etc. Just the language here.

Would my class make you a Python expert. By no means. As with all things, you become proficient through years of study and practice. It is my hope that my Python class would give you a good start on that journey.

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I just finished reading Blink: The Power of Thinking Without Thinking by Malcolm Gladwell. Perception is a fickle thing. When IBM created a text editor for their mainframe terminals, they ran into a weird problem. They we too fast. Changes were applied to the screen faster than people would perceive them. In order to nudge the brains of their users, they made the screen flash when a change occurred.

Working in an industry where people want high returns on low risk, I find myself at a loss to explain my sense of what is the right or wrong path. On more than one occasion. I have spend days preparing presentations to give decision makers warm fuzzies in dealing with issues which seem perfectly obvious to me. It takes days because there is a long way between perfectly obvious and the breadcrumb laden trail that people seem to need.

Unfortunately, you can’t Google yourself into a state of experience. So the question becomes, you do want to understand or just cover you ass? A paper trail does this quite nicely. At some point, you must make the decision.

If you want to glimpse the process, read the book.


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The Chrysanthemum and The Sword is an exploration of what makes the Japanese tick. At least from the standpoint of a early 20th century scholar. Ruth Benedict was one of that era’s foremost anthropologist.

I could go into the interesting discussion of how the American and Japanese cultures are compared and contrasted. Or how she describes the Japanese approach to Buddhism as being free of non-corporeal entanglements. I’ll leave those to the earnest reader.

What made the greatest impression on me was the lengthy and detailed exploration of on (恩) and giri (義理). These can broadly thought of as debt and obligation. In the west, we have a fixation on equivalent exchange. We like to pretend that there is nothing which cannot be fully bought and paid for. The Japanese fully recognize that this is not the case and have build a society around the concepts of overlapping obligations.

One that I have always found difficult to explain is that of shogimu (諸義務) or obligation to one’s teacher/mentor. This is always an asymmetrical relationship. The apprentice has nothing to offer in exchange in comparison to what they are given. In the west, we, as they say, “just take the money and run.” This is especially true in regard to the attitude of Googling for the answers. People have come to believe that they can monetize the collected knowledge of the world without cost to themselves. The problem comes not to this first generation, but to the second and finally fully realized in the third generation of internet users. The problem is that of who supplies the knowledge.

One of the big complaints of the pre-internet era was how big companies hoarded knowledge, making it available only at a premium. Should it not be free to all? Between the small band of developers (relatively speaking) who contributed to open source, the internet and google, we find ourselves in a place where you don’t need a master to monetize. And so rather than having to pay for software as a function of complexity and craftsmanship, we tolerate mediocre software because it’s free (with ads or in exchange for our personal information).

Meanwhile that high quality software we wouldn’t pay for when it was $400 with a year of updates, we will pay for when it’s a $120 annual subscription to a cloud service. Except that now if we stop paying, we lose access.

But back to the thorny question. Do we really believe that the Google-for-code and I-built-it-all-from-other-people’s-stuff crowd are going to give back to the community? What will happen when those who made all these goodies possible and available retire? If we take the C++ community as an example, there are hundreds supporting millions. This doesn’t scale.

Answers? Nope.

Thoughts? Some.

Personally, I’ve got a bucket full of on, so I should get back to it.

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There’s an old computer joke. “There are 10 kinds of people. Those who count in binary and those who don’t.”

I think there are two kinds of software developers, those who constantly reexamine their skill set and those who like working on antique cars. Mind you, I have nothing against antique cars. A good chunk of my personal library is dedicated to computer history. I love reading about how we got to where we are. I’ve been privileged to have been an observer to some of that history. That being said, ours is a discipline where answers are only as good as the half-life of their questions.

What’s the lifetime of a question you ask. It depends. If the question is “What is the length of the diagonal of a right triangle given the lengths of the other two sides?”, pretty damn long. On the other hand, if it’s “What is the best way to teach people how to write a program?”, well, tick-tock.

So, what does this have to do with shelfware? As it happens, a fair number of the projects I’ve worked on have become shelfware. Sometimes it was tech that the world just wasn’t ready for. Other times, it was a case of the tank shell and mosquito. Way too big of a hammer for the problem. (The bigger the hammer, the greater the resources needed to create and eventually wield it.)  There are those time when you’ve created a solution either without a problem or by the time you’ve got your solution, the problem has either been solved another way thank you very much, or simply no longer exists. (Remember how everyone got all wound up about Y2K?) Finally, some projects simply run out of time.

The interesting thing is that no one has as their goal the creation of shelfware. But, you know what? Shelfware happens. So, what is one to do? My favorite exercise in such is the post mortem. As with all things, I ask my two questions. “What do I have?” and “What do I need?” What I have is easy to answer. It’s one of the above cases. What I need is to determine is how it got there and what can I do to prevent the same sequence of events from occurring again.

Part of what comes from this analysis help me improve my situational awareness. Contrary  to popular belief, it is not enough to be good at what you do and expect that your insight, code or experience will suffice. Software is like an elaborate dining experience. If you can’t get the right dishes out to the right people in the right order at the right time, there will be hell to pay. By the same token, if your team isn’t able to fire on all cylinders, you’re bound to either lose the race or crash and burn. Eventually. Nothing is more pathetic than a software team who’s sole purpose is to sustain a dead product. But I’ll leave zombieware for another day.

Situational awareness is all well and good, but it’s a external. The more important thing for me is to see what I can do to up my personal game. This is not to say that I wait for a train wreck to do this, only that unlike my usual mental fitness endeavors (consuming all the Apple WWDC and other conference sessions, Coursera classes, etc.) and scanning the horizon for interesting directions that the industry is moving toward, I like to take stock of my core skill set.

It takes skill to drive a nail with one or two hammer strikes, but it’s far more efficient to use a pneumatic nail gun. More fun too. And you don’t need the same level of skill. The same is true of software development. In the early days of both C and C++, there were no libraries. Everything was handcrafted in every single program. The number of times any given problem was solved, to varying degrees of success and quality, is staggering. People created problem-specific solutions which were then expanded upon for other projects. As usually occurs, they accreted unrelated and inseparable bits. By the time an opportunity to refactor the code base into some semblance of modernity, it is usually deemed too great a risk. So, while you’re becoming the expert in dinosaur care and feeding, the next generation of developers has been tinkering away with the latest and greatest.

I know, I know, the fundamentals you say. Okay. So, how about you show my your linear and logarithmic interpolation skills? No? You used Matlib perhaps? Hmm. Well then, how about explaining why 8.3 file names are storage efficient? Surely balancing binary trees. But wait, don’t you get that from maps in C++ and just about every other modern language? The finer details of memory allocation and pointers? Have you ever talked to a Java or Python developer? Binary tree common ancestors? Understanding how to implement that must be important. Seriously? In thirty-two years of developing software full time, I have never needed to re-balance a binary tree or find a common ancestor, except for interview questions. Which probably makes me unqualified to create the next great search algorithm. And that’s okay. All these answers reached their half-life long ago. Well, maybe not Tango trees. But they’re still a solution looking for a practical problem.

Over time, all these problems have had their solutions codified into either the language or the language’s standard library. This is a good thing. Map / reduce was once new and shiny. Now it’s built into Apple’s Swift language. I would much rather people who live to create the best sorting algorithm do so. That way everyone benefits. I don’t want to have to care if my data is partially sorted, whether I have fifty or fifty million items, if I have strings or structures. I want to sort my data in order to do something else with it, not because sorting it amuses me. By the same token, no one in their right mind would even consider creating a GUI from the ground up today. I’ve personally had to do this more than once. It’s a boat load of work and not portable. Why should data structures and their manipulation be any different? After all, we create abstractions so that we can handle problems of greater complexity.

So, if I’m not looking at these, then what?

Currently, three things have my attention. The first is Apple’s Swift. I’ve been watching this language develop over the past two years into a nearly complete solution for application creation. The second is C++11/4/7. I started working with C++ in the mid ’90s and it has managed to keep pace with developments in the processor and compiler spaces. Anyone who is still writing code as if nothing has changed since C++03 really needs to look at C++14. Between auto, atomics and memory handling changes, C++ continues to bring its A game. Finally, and most importantly, I’ve been looking at secure coding. Having recently taken the US CERT secure coding course, I was surprised at the level of code exploitation possible because of patterns of coding that go back decades. If OpenSSL has issues being exposed on a near weekly basis, would we expect that other major pieces of our infrastructure are not equally at risk?

To remain relevant in the world of software development, one must not only be competent in the current use of technology, but also be actively learning and incorporating the advances in the materials (languages / frameworks), methodologies (paradigms) and tools (editors, compilers, IDEs, etc.). Not to mention other peoples’ code.

Everything can and should be learned from,  even shelfware. It’s merely experience without exposure.

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