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Evaluating E-Learning: A Lesson From Pharmaceuticals
“You should be shot,” declared Bob. My presentation was on how to sell e-learning to upper management, and the real-world case study I presented emphasized some dramatic, multimillion dollar cost savings. “E-learning should not be about cost savings,” Bob
I’m not talking about the obvious and training-centric four-level evaluation model we all grew up with. I’m talking about rigorous control-group studies that look at endpoints and search for statistically significant improvements.
Let’s look at how pharmaceutical companies develop new products. If they want to launch a new cholesterol drug, they sign up hundreds of patients and split them into three groups. One group gets a placebo (sugar pill with no health benefits), a second group gets a competing drug, and the third group gets the new drug. If the new drug is no better than the placebo or the current treatment, they lose. If they can statistically prove that it is more efficacious than current treatments, they’ll have a billion dollars.
So how can we adopt this comparison study strategy?
- Have a new e-orientation program? Run half of your new hires through the “old way” and the other half through the new program, then measure employee engagement.
- Want to roll out a new selling skills program? Why give it to everyone if it doesn’t work? Put a large group of your sales reps into the new program, leave everyone else alone and look for sales improvement over a full sales cycle.
- Designing a course? What are the measurable benefits of audio and video versus text? Of simulations versus tutorials? Of e-learning versus blended learning?
Critics will say we can’t prove e-learning’s effectiveness because there are too many other external variables on performance. Yet drug discovery researchers will be the first to admit that there is no such thing as perfect science. They too struggle with factors like inadequate sample size, demographic factors and unrelated symptoms. Choose your control groups carefully to minimize external factors and then look for variance in outcomes that are statistically significant (i.e., beyond mere random chance).
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