Dave's Whiteboard

Two virtual conversations (mostly via Twitter direct messages) got me thinking about means, ends, cashews, and potato chips.

I tend to see “training” as a shared-misunderstanding term–everyone thinks they know what everyone else means by “training.”  It looks like a convenient label, but it’s a hidden discrimination, a kind of conversational wrapping paper folded over who knows what.

Kind of like “diet.”  That can mean “rules about food,” like “eat only vegetables beginning with R.”  Or it can mean “how I manage the way I eat.”  These things aren’t either/or; they’re a combination of procedural knowledge (a gram of fat equals 9 calories) and cognitive strategies (most of the time, I think of a serving of potato chips as one bag–regardless of the size of the bag).

What’s that got to do with anything?  In one of my conversations, I was grumbling about the icebreaker/ten-quick-tips/put-fun-in approach to training.  To me, these are sideshows.  I don’t have a problem with organizational training; it makes sense to think about specific skills and knowledge that clearly contribute to organizational results.

Formal training, though, can’t do the job alone.  By definition, it’s time-limited, which means you probably can’t practice recurrent skills enough.  You may not have enough time (or have done enough analysis) to have rich, relevant whole tasks.  And often training doesn’t address the real causes of performance gaps at work.

In the long run, you can’t grapefruit-diet your way to health; you can’t train your way out of poor performance caused by incompetent information systems or unrealistic standards or worthless feedback.

Not everyone (client or practitioner) is ready to act with a performance-improvement focus.  And perhaps some of the time you don’t have to, though it’s not a bad goal to have in mind.  I see practical, training-focus efforts like the bags of cashews they sell at Trader Joe’s: a big bag full of little bags.  Each little bag has maybe 125 calories’ worth of cashews.  Buying that packaging works for me; the individual bags slow me down enough that I don’t eat a cup and a half of cashews at ones.

Not so potato chips.  For me, a serving is one bag–regardless of size.  So I manage that performance by not buying bags of chips often.

Even when I’m working in a constrained, formal-training situation, I try to use performance improvement as a kind of workplace mindset.  The ultimate goal is not an assessment or a record in an LMS; it’s a person who can produce worthwhile results on the job.

CC-licensed images of rhubarb and cashews both by FotoosVanRobin.

In a discussion on LinkedIn, someone asked what the “killer app” for elearning would be.  My response:

Maybe it’s an application that automatically crashes if you have ten page-turner screens followed by five multiple-guess questions. Or if you even think of including a Jeopardy-style game.

While I understand the desire for a killer app, I think that it’s almost impossible to see beforehand.  To reach way back to 1979, no one anticipated that the thing to pry personal computers out of the hands of engineers and hobbyists would be VisiCalc, the great-grandparent of spreadsheets.

What made VisiCalc a killer app was a goal essentially outside the world of computer engineers and programmers: an ordinary business person who had something of value to accomplish could do that without having to know anything (much) about creating hardware or software.

Another comment in that discussion on LinkedIn says that “one of the Greek philosophers” defined the art of learning as “what takes place when a very young man sits on one end of a log, listening to a very old man on the other end.”

I’m pretty doubtful.  Aristotle may have been a killer philosopher, but as Bertrand Russell said, Aristotle also believed that women have fewer teeth than men do.  “Although he was twice married,” Russell noted, “it never occurred to him to verify this statement by examining his wives’ mouths.”

More seriously, I think some learning can begin in that mythic setting, but only if what the old man says sparks activity in the brain of the young man.  Otherwise, you’ve got log-based lecture, not necessarily the most fertile ground for learning.

If you moved from Greek philosophers to Benjamin Bloom, you’d be talking about something like the receiving level of the affective domain (passively paying attention) or the knowledge level of the cognitive domain (knowledge of facts, terms, and so on).

I’m not fulminating about lecture per se; there are plenty of situations in which a non-interactive vehicle like a book, a podcast, or a video can foster learning.  Almost always, I think, that’s in combination with things like deliberate reflection, trial-and-error, support in the form of models or cognitive strategies, guidance in the form of procedures–

In other words, a range of approaches and tools appropriate to the learner, to the knowledge and skills, and to the context in which the learner wants to apply them.  For me, “killer application” isn’t a synonym for “cool software;” what it really means is “putting learning to work and getting learning to work.”

Series: Ten Steps to Complex Learning

Step 9 in Ten Steps to Complex Learning is “analyze prequisite knowledge.”  As with Steps 7 and 8, van Merriënboer and Kirschner are talking about procedural information, the knowledge that you apply in the same way to different problems.

(You can click the image to see a chart with all four components and all ten steps.)

The prerequisites are the things you have to know in order to perform these recurrent skills.  vM&K argue that you have to analyze the cognitive rules (Step 8) in order to find out what the prerequisites might be.

Facts: three levels down

Way back in Step 6 (analyze mental models), Ten Steps discussed domain models for non-recurrent skills (the skills you apply differently to different problems).  One way to think of a domain model is that it’s a high-level abstraction.  As you move closer to the job, you can discern elements like conceptual, structural, and causal or functional models; moving closer still, you get to individual facts.

When you’re talking about recurrent skills, the middle and bottom levels are more relevant to the algorithmic nature of procedural information.  As with cognitive rules, this is a step you might be able to avoid if the rule-based information already exists in manuals, traing programs, and similar forms.

For that middle level, vM&K talk about concepts, plans, and principles. Concepts are descriptions and classifications, like the concept of a field in data entry, or the concept of offsides in soccer.

Plans (another peculiar vM&K term) “relate concepts to each other in space…or in time.”  What I think they mean by plan is a kind of map or arrangement, like an electrical circuit or a standardized format.

One of their examples is how you present the mean and the standard deviation in a research paper: M=x.xx; SD=y.yy.  The idea is that as you go about writing your paper, that arrangement is the plan for that particular item.

Plans can also be scripts, which I take to mean small recipes for accomplishing some task.  The order of sections for your research paper is a kind of script.

Finally, principles related concepts to one another in cause-effect or as part of an overall natural process.  (The difference is that a natural process is continuous and doesn’t have a fixed start point, like the evaporation/condensation cycle.)

vM&K say that learning a principle helps a person move from rote memory to understanding.  If you learn principles for performing subtraction, for example, you don’t have to memorize how to subtract every number from every other number.

Facts and features

Ten Steps takes a side trip into logic, or philosophy, or something.  The chapter suggests that one way to identify a concept is to “all the facts that apply to its instances.”  These will be “propositions” with a “predicate” and at least one “argument.”

For example, in word processing, a column is elongated.  “Column” is the argument (the subject), and “elongated” is the predicate (the relationship).

No, I didn’t quite get it, either.  In another example:

A text processor [word processor] may construct columns using the table function.  This is a proposition with three arguments (text processor, which is the subject; column, which is the object; and table function, which is the tool connected with the predicate “construct”).

vM&K say that these propositions are the smallest building blocks of cognition; “there are no facts that enable the learning of other facts.”

Let’s see–let’s take the fact that Paris is the capital of France.  As the authors say, in a sense a fact has meaningless, arbitrary links.  You could say “Paris is the capital of France” without knowing anything about Paris, capitals, or France.  And even if you do know about Paris and France, you’re not necessarily better able to learn other facts about Paris or about France.

Facts and concepts

Concrete concepts may require physical models like schematic diagrams, exploded diagrams, and similar tools to help learners develop better mental models.  Along with the concept of an electrical resistor (opposes the flow of electricity), examples of its physical form can help the learner.

There’s actually an interplay here, and this may be part of that confusing conceptual-model business.  Not all resistors use color-coded bands to indicate the amount of resistance.

As you’ve seen with conceptual rules, vM&K say that prerequisites should be aimed at the entry behavior of the target learners.   You’d think it’d be easy to go too far–think of computer-application training that insists people know what radio buttons are, not just know how to use them on the job.  The authors are also concerned that it’s all too common to overestimate the amount of prerequisite knowledge that learners already have–the familiar curse of knowledge.

Design implications

As with other types of knowledge, learning about prerequisites can benefit from an analysis of misconceptions.  Think of language barriers and George Bernard Shaw’s observation that Americans and Britons are two people separated by a common language.

In the U.K., a “scheme” is a plan; the British government is proposing a scheme for regulating asset-backed securities.  In the U.S., the word “scheme” almost always has a connotation of trickiness or outright deceit, enshrined in “Ponzi scheme.”

The Ten Steps consistently recommends presenting prerequisite information just-in-time, and on a just-enough basis.  In addition, by focusing attention on misconceptions, well-designed materials can help people learn.  I haven’t owned a Volkswagen since I was an undergraduate, but still recall John Muir’s manual for keeping my Beetle alive.  Muir consistently put “front is front” into instructions for dealing with engine problems.

Beetle engines were in the rear of the car.  Muir learned as he helped people work on their cars that they often shared a misconception: “front” to them meant “closer to the outside.”  If he said, “change the front plugs,” they’d start changing the ones closest to the back of the car.

So, “front is front” was a prerequisite piece of knowledge: in this manual, when you’re working on the engine, “front” means “the front of the entire car.”

Other techniques to help learners master prerequisite knowledge:

• Slower fading–in other words, maintain a relatively high level of support during the early learning tasks.
• Multiple representations.  You might provide both a verbal or textual description and a diagram, photo, or illustration.
• Compare/contrast.  As a design element, vM&K urge you to have learners practice applying the new skills.  Working with effective and ineffective concepts helps them better recognize the difference when it matters.

That’s it for Step 9.  Next time, the tenth step (design part-task practice).

CC-licensed images:  not-quite-level steps by R.B. Boyer;
normal distribution with standard deviations from Wikipedia;
automatic flusher sign by tico24.

Series: Ten Steps to Complex Learning

The previous two posts in this series about Ten Steps to Complex Learning were the first ones dealing with procedural information.   Step 8, analyze cognitive rules, continues that focus.

(You can click the image to see a chart with all four components and all ten steps.)

In the Ten Steps, cognitive rules and procedures are how you carry out recurrent tasks in a correct way.  “Recurrent task,” to authors van Merriënboer and Kirschner, means one you perform the same way every time.

x = 23 – ( 7 * 14 ) / ( 81 – ( 6 / 2) ) + 9

The procedural part of solving that math problem involves a set of rules that for the most part ignore the numbers in the equation.  In other words, procedural rules are algorithmic, not heuristic; they’re a set of steps and not a handful of rules of thumb.

vM&K says that cognitive rules are “strong methods.”  If you follow the rules, you get the right result.  Perform multiplication or division before addition or subtraction; perform operations inside a parenthesis before those outside the parens.

Strong methods tend to have limited flexibility; they apply in very specific circumstances.  Rules for arithmetic don’t do you much good in spelling.

Spelling out the rules

As with supportive information (for non-recurrent tasks), you can often find procedural information in existing documents, training, and other forms.  If not, you’ve got to do the spadework because of what your analysis can provide:

• It highlights prerequisite knowledge essential for carrying out the rules.  (Step 9 will deal with analyzing prerequisites.)
• Specifying if-then rules gives you the building blocks for designing the procedural information your learner will work with.
• Your analysis also becomes the foundation for part-task practice (the subject of Step 10) to build automaticity in the use of procedural information.

Working with existing documents and interviewing expert practitioners can take two main forms. 

Rule-based analysis applies when there isn’t a specific order in which to perform the steps.  vM&K have an extended example about rules for efficiently stacking a set of different sized buckets; it’s similar to rules you’d use for solving the Towers of Hanoi puzzle.

You use an information-processing analysis when order does matter, such as following a fault tree when troubleshooting.  To me, this is a good way to highlight the differences between algorithmic and heuristic approaches.  Many experts have so automated the rule-based stuff that it seems intuitive to them.  Still, when you’re dealing with entry-level behavior, vM&K’s step-by-step approach  enables novices to achieve the desired result in any situation to which the procedure applies.

Note that it’s important to validate your analysis.  That involves at least two dimensions, I think: having exemplary performers review the resulting rules and procedures, and having typical learners try them out in context.

How specific is a rule?  (How long is a ladder?)

Step 8 says that you shouldn’t get too granular in spelling out the procedural rules.  You don’t want to say, “Retrieve X from your long-term memory.”  On the other hand, vM&K say it’s easy to overestimate the prior knowledge of your learners.  All the more reason for quick prototyping, I’d say, if you’re actually going to revise the material based on the tryouts.

Specificity is one of the things that distinguishes procedural tools from supportive ones.  A flowchart or job aid for reporting airplane flight status (arrived, departed, delayed, ontime, early, late) could be extremely specific; one for dealing with difficult customers would have to be more general.

(And while using the customer-service job aid wouldn’t guarantee correct performance in every situation, using the flight-status job aid would.)

Errors and malrules

From experts and existing procedures, you can build the correct rules.  It’s useful (to say the least) to know common errors and misunderstandings (or “malrules” ); these become important resources for learning design.

Errors, in this point of view, result from things that are difficult to apply, easily left out, and so on.  Double-clicking a mouse is “obvious” to an experienced computer user, but the novice has to master the time between the first and second click.  (Well, I did double click, but nothing happened…)

A malrule, in contrast, is an erroneous procedure.  It could be the result of what vM&K call intuitive models, as discussed earlier in this series. It seems logical to shut down the computer by switching the power off; that’s what you do with other electronic devices.

Now what?  Cognitive rules and design decisions

Cognitive rules focus entirely on how you deal with the recurrent aspects of tasks.  You need to examine the rules and the target audience to determine prerequisite knowledge.  Knowing the order of math operations isn’t sufficient if you can’t multiply.  Knowing that Firefox is your browser won’t get you far if you don’t know how to launch it.  (The next post in the series will go further into prerequisite knowledge.)

“In a psychological sense,” the Ten Steps says, “recurrent constituent skills are analyzed as if they were automatic psychological processes.”

Thats because by design you want to encourage compilation (the building of specific rules by the individual) and, often, automaticity.

So you explicitly present the rules or procedures just in time, along with the task, following the guidelines mentioned with Rule 7 (previous two posts in this series).  That’s where the well-designed displays come in.  They don’t fiddle around with nice-to-know stuff; they don’t needlessly split attention.

You can also use the rules to design effective demonstrations of the skills in question, and to develop corrective feedback.  “Wrong!” isn’t usually good feedback, though it’s important to make clear that the response is incorrect.

Errors and malrules–easy to uncover through tryouts–offer potential for design.  You can focus learners’ attention on rules or steps that are prone to error.  (“This is tricky, and you may get it wrong the first few times.”)  Displays of procedural information can include error-recovery steps.  And providing many demonstrations of steps with a high chance of error means that learners have more opportunity to perceive.

Next time: Step 9, analyze prerequisite knowledge–what you need to know in order to perform recurrent aspects of a complex task.

CC-licensed images: Pythagorean-theory image by tobo;
Towers of Hanoi image by chuyanyatyuet;
computer-competition practice photo by iBjorn.

A few weeks back, “Scott from Google” asked 50 people in Times Square, “What’s a browser?”

Boy, aren’t people dumb?

I don’t think Scott from Google thought that, despite the end-line telling you that less than 8% of people interviewed knew what a browser was.

If you listen again, though, notice how they see the term “browser.”

• A website you can search
• A search engine
• It’s where I search through the find things
• I use the Yahoo!
• The internet is where you find anything
• A way to get on

In other words, for the people in the video, the computer and its software are a means, not an end.  The car owners in this group most likely couldn’t tell you if their vehicle has an alternator, or even the number of cylinders, but they can probably use the car to get from home to work.

(Hey, Scott–what’s a gerund, and which pronoun case do you use with one?)

It might help someone in certain circumstances to be able to describe a browser and distinguish it from a search engine.  For the people in the video, that seems a low-priority task.

If Scott had had a couple of computers on a table, with  half a dozen browsers, and asked people, “Can you  find the price of Google stock?” (or  download a video from YouTube, or tell him who’s the president of France), I’m guessing the majority could–if the task related to the kinds of things the people normally do on computers.

What annoys me is not the video itself, but the overall mockery in the YouTube comments.  It’s easy for those in the know, detail-wise, to decide that others ought to know those same things.

Just think how much richer your life would be if you had known the DOS FDISK command.  (If you have no idea what that is, hardly anyone else did, either.)

It’s really a cautionary tale for how people do things, and how they learn.  Look at the whole task and at the context.  If you’re going to be tweaking lots of software, then, yes, you probably do want to know what a browser is; you may even, like at least one person in the clip, use more than one.

On the other hand, my 90-year-old mother accesses two or three blogs, does some cautious shopping, and even checks her bank and credit card balances online.  She has no idea what browser she uses.  She’d think the word meant someone who says to the store clerk, “Thanks, but I’m just looking.”

Which, come to think of it, is what she does online.

CC-licensed browser image by shanta.