Friday, 30 April 2010

Learning: Learning Models

Continuing my series on software developers learning, here is a brief look at a couple of very interesting and useful learning models. (See also learning styles on Wikipedia for other interesting material).

There are a wide variety of interesting and applicable models, and so this investigation is naturally not an exhaustive survey of what might be applicable to us, as software developers.

This material has been adapted and expanded from my March 2010 CVu Professionalism in programming magazine column.

Learning models

There are a number of very illuminating models of learning that have been constructed by educational psychologists. The Dreyfus Model of Skill Acquisition is a particularly interesting example, postulated by brothers Stuart and Hubert Dreyfus in 1980 whilst they were working on artificial computer intelligence [A Five-Stage Model of the Mental Activities Involved in Directed Skill Acquisition. Stuart E. Dreyfus; Hubert L. Dreyfus. Storming Media. (Available from:]. After examining highly skilled practitioners in fields such as airline pilots and chess grand-masters, they identified five specific levels of understanding:

  • Novice A complete newbie. Novices want to get results fast, but have no experience to guide them in doing do. They look for rules they can follow by rote, and have no judgement to tell whether those rules are good or bad. Given good rules (or luck finding suitable resources on Google), novices can get quite far. Novices have no knowledge of a topic (yet).

  • Advanced beginner At this level, some experience that has lead to learning; you can break free from rules a little and try tasks on your own. But perception is still limited, and you'll get stuck when things go wrong. At this level there is a better understanding of where to get answers (you know the best API references, for example) but you are still not at a level where you can comprehend the bigger picture. The beginner can't focus out irrelevant details, as far as they're concerned everything and anything could be important to the problem at hand. Beginners rapidly gain explicit knowledge – the kind of factual knowledge than can be easily written down and articulated.

  • Competent This stage sees you with a mental model of the problem domain; you've mapped the knowledge base, and have begun to associate parts and understand the relative importance of different aspects. This big picture view allows you to approach unknown problems, and plan methodical routes into those problems rather than diving in and hoping rules will get you to a solution. This is a good place to be.

  • Proficient people move beyond competency. They have a much better understanding of the big picture, and are frustrated with the simplifications that the novice needed. They can correct previous errors and reflect on their experiences to work better in the future. At this point you can also learn from other's experiences and assimilate them into your body of understanding. Proficient people can interpret maxims (as opposed to simplistic rules) and apply them to a problem (e.g. they know how and when to apply design patterns). Now it is easy to identify and focus only on the issues that really matter, confidently ignoring irrelevant details. Here we see the person has gained significant tacit knowledge – knowledge that's hard to transfer by exposition, that is only gained by experience and deep understanding.

  • Expert This is the pinnacle of the learning tree. There are very few experts. They are authorities on a subject; they know it completely, and can use this skill interlinked with other skills. They can teach others (although they probably teach competents better than novices as there is less of a disconnect). Experts have intuition, so rather than needing rules they naturally see an answer, even if they can't articulate why it's the best solution.

Why is the Dreyfus Model interesting? It's a revealing framework to understand where you currently stand in the mastery of a topic, and help determine where you need to get to. Do you need to be an expert? Most people are competent and this is satisfactory (indeed, a team of experts would be far too top-heavy, and probably dysfunctional).

It also illustrates how you should expect to be solving problems at each stage of your learning. Are you looking for simple rules to apply, hungrily gathering maxims to draw experience from, or intuitively sensing answers? How much of a “big picture” view do you have of the topic?

The Dreyfus model is also a very useful aid for teamwork. If you know where a colleague sits on the notice-expert spectrum you can better tailor your interaction with them. It will help you learn how to work with other people – should you give them some simple rules, explain some maxims, or leave them to weave new information into their broader understanding.

Note than the Dreyfus model applies per skill. You may be an expert in a particular topic, and a complete notice in another. This is natural. And should also be a source of humility – even if you know all there is to know about Behaviour Driven Design, you may no nothing about the Joe Bloggs Test Framework. It should excite you that there is something more to learn that may enhance your expertise in BDD, whilst keeping you humble that you aren't an infallible expert in every subject! No one likes a know-it-all.

Shu-Ha-Ri is another interesting model of learning that originates in Japanese Martial Arts. It is a set of three terms describing the stages a student goes through to to achieve mastery in their discipline, roughly translating to Learn, Detach, Transcend. In the Shu phase, the student studies under one teacher; he does not understand enough to mix concepts from different streams, but aims to imitate the teacher precisely. He must build up experience in the field, absorbing the teacher's explicit and tacit knowledge. Once he has a full understanding of the techniques, the student reflects on them in the Ha phase. The knowledge he has learned will not change, but he can interpret, reflect and explore the deeper meaning. Finally, the student progresses to Ri, where he moves beyond studentship; he has original thoughts, and becomes a master himself. He now has the skills and experience to innovate.

Heed this model. Don't try to transcend and innovate until you have completed the Shu phase of your learning (remember to be aware of unconscious incompetence) and have completed the Ha phase of understanding the knowledge.

In my recent conference presentations on this subject, I've also looked at the fashionable "craftsman" model, and seen how the historic guilds system of the pre-industrial revolution applies (and doesn't apply) to our learning. It's an interesting model that bears comparison to shu-ha-ri.

Monday, 26 April 2010

Software Developers Learning: Slides available

The slides from my ACCU 2010 presentation on software developers learning, poetically entitled Stood at the bottom of a mountain looking up are now available.

This was a very popular presentation, and the room was packed, so thanks to all those who came along and squeezed in. I've had a lot of great feedback, so I'm glad that the talk was useful. As promised, there are a lot of references at the end of the slides that you can follow for more information.

The slides are available on SlideShare here.

If you'd like me to give this talk at your site, user group or conference, then please get in touch.

iPhone Development slides available

The slides from my ACCU 2010 presentation on iPhone development are now available, for those who wanted a copy.

You can view them on SlideShare here.

If you'd like me to give this talk at your site, user group or conference, then please get in touch.

Sunday, 11 April 2010

Learning: Understanding the brain

Psychologists, biologists, and other similar evil geniuses have studied the human brain for years, and have formulated many models of brain behaviour and models of learning. There is an overwhelming quantify of research on the subject (some quite contradictory).

Understanding (at least at a basic level) how the brain works, and some of its characteristics will help us, as software developers, work out how to learn most effectively. Even a rough understanding of some basic principles will help us unlock the potential learning and processing capabilities of our grey matter.

The left brain/right brain split. We hear a lot about the “left-brain” and the “right-brain”. These terms have entered pop culture, and you are as likely to hear them discussed by artists and CEOs as by psychologists. It comes from research (pioneered in the 1970s by Roger W Sperry who coined the left/right brain terminology) discovering that our mental activity is split between the two hemispheres of the brain; either side controlling a different mode of thinking (Lateral specialization in the surgically separated hemispheres. R.W. Sperry. In Neurosciences Third Study Program. F. Schmitt and F. Worden (Eds.), Cambridge: MIT Press 3:5-19 (1974).).

The left hemisphere tends to govern analytic and logical activities (language, symbolic representation, rational deduction, the linear thought process) whilst the right hemisphere performs non-linear processes (characterised as non-rational, spatial, holistic, and identifying spatial relationships).

The common view is that people rely more, or tend towards using, one side of their brain rather than other. Artistic, creative types favour their right brain; academics and office-workers favour the left. Apart from reinforcing a social stereotype, this is not strictly accurate since the split of brain activity is not pure laterally biased. Several psychologists have since tried to come up with other terms, but popular culture has latched on to left- and right- and we're stuck with them.

Both parts of our brain and both modes of thought are essential. In order to think and learn effectively we must be able to bring both “sides” of our brain into use. Since programmers tend to lean towards the left-brain mode of operation, we must learn to introduce more right-brain thinking into our regimen; this requires us to find ways to “dial down” our left brain activity to give the other side a fighting chance.

I'll describe some ways to do this in later postings. There's a lot more that could be said on this subject; it is a huge topic, and well worth investigating more if you are interested.

Personality type affects learning style If you favour the right-brain mode of thinking you will learn best when presented with patterns and a holistic view of a subject, rather than a serial stream of information. You'll prefer to make associations and understand overarching themes. If you favour the left-brain you want a linear rational presentation of the topic. You'll prefer to assimilate facts than to have a grand story told you.

Clearly your brain wiring has a radical effect on the most effective style of learning for you.

There are many models of personality type, perhaps the most famous being the Myers-Briggs Type Indicator (MBTI). MBTI classifies your personality along four axes. Your personality type will effect how you associate knowledge, and how you best learn. One MBTI axis is Introvert-Extrovert. Introverts would rather learn on their own – they need private mental space to work things through. Extroverts learn well in groups where they can discuss and feed off the ideas of others. Another interesting axis is Sensing-Intuition. The Sensing personality is the classic left-brainer who emphasises facts and details. They must complete one task before moving on. Intuitive people are right-brainers who use their imagination more. They can move on without completing a task or before they understand everything about a subject.

Understanding your particular personality type (there are many MBTI tests available on the web, for example) will reveal specific ways to make your learning routine maximally effective.

Understand how you learn best.

Memory fades. There was a time when psychologists believed that a memory, once made, was permanent. However this is not the case: a certain enzyme (PKMzeta) is required to keep synaptic connections valid. Lose the enzyme, you lose the memory! (See this for more info).

Our memory is also fallible in other, less chemical, ways. The brain isn't perfect. You'll notice that old memories can change and distort in your mind. (So it's probably not true that things aren't as good as they used to be.) Memories and opinions can easily warp as we recollect them, to fit our current preferences or preconceived notions. We can very easily implant our own false memories (unconsciously) or become subject to false suggestion from others.

In order to keep your memory active, it must be refreshed – constantly read and exercised. If you don't need and employ a skill then you will use it.

Use your knowledge. Or you'll lose it.

Memory grows. It was originally thought that mental capacity decreased over time; that humans start off with a fixed number of brain cells and over the course of a lifetime this number decreases (due to the ageing process, damage caused by trauma, or other abuse – like excessive alcohol intake).

This was determined by scientists studying animals in laboratory cages; they saw no sign of neurogenesis – the growth of brain cells. But this was simply because a brain devoid of stimulus need make no new connections. The test subjects' brains didn't expand because there was no need to; life in a cage stunts brain growth. That's a pretty damning inditement of modern cubicle working!

In the early 1990s psychologist Elizabeth Gould discovered that in suitable conditions (an environment with stimulation and opportunities to interact and learn) the brain is perfectly capable of growing neurons and making new connections (Neurogenesis in the Neocortex of Adult Primates. Elizabeth Gould, Alison J. Reeves, Michael S. A. Graziano, Charles G. Gross. In: Science 15 October 1999:Vol. 286. no. 5439, pp. 548 – 52.). Other subsequent studies have drawn links between exercise and increased brain growth.

So that's good news – you have a practically limitless ability to expand your mind. And the scientists have proved that you need stimulation do to so! Break out of your cubicle, and feel free to drink a beer!

Mental state effects learning. Factors such as stress and a lack of sleep will clearly contribute to an inability to concentrate, and so will degrade your ability to learn. Your mental attitude also dictates how well you will learn. Psychologist Carol Dweck's research (Mindset: The New Psychology of Success. Carol S Dweck. Ballantine Books. ISBN: 0345472322) shows that students who believed that they couldn't increase their knowledge were not able to do so. Those who believed they could increase their mental capability were easily able to.

Believe in your ability to learn.

People who enjoy learning naturally learn more. People who want to learn, learn more. Is this the power of mind over matter? Perhaps it is; and it's something we need to exploit. I've long argued that our attitude effects the quality of our work. This is the central theme of my book Code Craft (Code Craft: The Practice of Writing Excellent Code. Pete Goodliffe. No Starch Press. ISBN: 1593271190).

I will be speaking more on this topic at the ACCU conference next week.