Robust practice: where corporate learning misses the train

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In the world of organizational learning, there’s always another bandwagon. In fact, they have to widen the L&D Highway every few years to accommodate late-model bandwagons, as well as service vehicles full of batteries, charging cords, and templated reports. Which is great–if you’re in the bandwagon-supply business, rather than the far less trendy business of helping people accomplish things on the job.

I work for an organization where much of the staff works with complex computer systems. In our case, the systems help us administer pension programs — enrolling people when they start a job covered by one of our plans, producing annual statements of benefits, calculating estimates, processing transactions related to salary and service, and managing all the pension-payment transactions.

Hundreds of thousands of people–I suspect millions, actually–work in similar jobs in any industry you can think of. I am pretty familiar with hotel, airline, and passenger rail reservation systems, and I’ve helped train people to use systems for managing inventory, conducting pharmaceutical trials, monitoring accounts in the consumer packaged goods industry, trading natural gas, and tracking shipping containers.

Almost none of these systems had a way for people to practice tasks in a robust way.

By robust practice, I mean features or capabilities that let people practice complete tasks, as they would on the job, without risk to data, resources, vendors, or clients. It’s my contention that most large corporate systems, although they’re the workhorses of the organization, have no way for someone to practice what he’s supposed to do in the production system–except by working with real data.

I say “production system” as shorthand for the ability to reserve actual airline seats or handle actual pension benefits or process actual bank loans. And I’m going to say “practice system” when I mean the ability to exercise the same steps and skills without reserving actual seats, handling actual pensions, or issuing actual loans.

In a financial system for managing bank branches, for example, if you wanted to practice the steps to process a car loan, you had to pretend you were issuing a loan to yourself. And you really had to remember not to click approve, or customer-you would have a loan.

“Don’t push Approve!” is good guidance in that situation, but a lousy way to train someone in loan approvals.

Is this your experience, too?

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Simulations are at best a half-measure. (At worse, they’re a fantastic way to set money on fire.) It takes forever and two weeks to develop a simulation, and the Thursday after it’s released, IT relabels the Pending tab and adds two new buttons on the Estimation form.

(Added to original post: a comment from Clark Quinn on Twitter made me realize that this last paragraph could be misconstrued. I was thinking of simulations of some complicated corporate computer system–in other words, a standalone imitation of the production system, one that requires at least as much maintenance as the production system does.)

True robust practice doesn’t mimic the production system; it mirrors or piggybacks on production’s capabilities while ensuring that anything that goes wrong in the practice system stays in the practice system.

At Amtrak, we created a set of imaginary passenger trains–the “training trains.” They had robust practice features, and also safety features.

Robust features:

  • Training trains ran on the same routes as actual trains, with the same schedules, fares, and accommodations.
  • The set of training trains contained every accommodation available, so you could practice reservations that might rarely or ever come to you otherwise.
  • Training train schedules, fares, features, and services were created using production-system data, so the training trains always reflected real-world conditions.
  • Practice IDs could retrieve such production-system information as schedules, fares, routes, on-board services, and operating status (“Is train 353 on time?”).
  • You could log into a practice ID from any terminal connected to the production system.

Safety features:

  • A person using a production ID could not display the training trains. (You couldn’t make a reservation for a real person on an imaginary train.)
  • A person using a practice ID could not reserve space, create reservations, or issue tickets on an actual train.
  • Practice users could issue the “print ticket” command, but the actual printed ticket clearly read TEST TICKET / NOT GOOD FOR TRAVEL.
  • Practice users could not enter or alter production-system information — e.g., they could not report an actual train as departing on time or change the hours of a ticket office.
  • To log into a practice ID, you had to log out of your production ID.

Additional safety measures guaranteed, for instance, that imaginary revenue from training train reservations was not counted as actual revenue in the production system.

We try so hard as learning professionals to create authentic, high-value formal training. And we talk a lot about the problem of transfer, the need for spaced practice, the value of whole tasks, and the like. But we don’t seem to advocate for measures that would deliver robust practice to the workstation, and many of us are a little uneasy about what was described to me as “letting people run around inside a practice system.”

Such running around, which I think of as “deciding what I want to practice, then practicing it,” seems to me to have far greater potential for performance support than run-of-the-mill elearning, no matter how many images it has of people in suits, talking on phones.