de
Measuring learning success is considered right but tedious. The objections are almost always the same, and most of them contain a grain of truth.
“We already measure, the LMS shows completions.”
The LMS shows who clicked through a course. That matters for mandatory training, because proof counts, and that is what it is built for. It says little about learning success.
How little is shown by a comparison: classic mandatory courses reach completion rates of around 25%, microlearning and interactive formats up to 90% (Continu, 2025). A high completion rate is mainly a sign that the format fits. Whether someone works differently afterwards is another question.
“What was learned cannot be separated from everything else.”
True, never completely. If the error rate drops after a course, it could also be a new tool, a new team lead or the season.
That is no reason not to measure, though, but a reason to phrase modestly. A statement like “after the training, questions in this department fell by a quarter, and nothing else changed in the period” is honest and usually enough for a budget decision.
“Our business units have no metrics for this.”
Mostly they do, just not under that name. How long does a new hire need to work independently? How often does an expert answer the same question? How many cases go into rework? These numbers often sit in ticket systems, quality reports or the team lead’s head.
Three such metrics have proved particularly useful in our projects: onboarding time, questions to knowledge holders and rework. In Omnora projects we see 15 to 30% shorter onboarding, 20 to 40% fewer questions to experts and 10 to 25% less rework, by our own measurements.
“It costs more effort than the training itself.”
Only if you make it too big. A full evaluation model for every course is indeed disproportionate. A single metric, measured before and after, is not.
The trick is timing. Look for the metric only after the training and there is no baseline left. Define it before the start and the measurement afterwards takes half an hour.
“Management only wants to see an ROI anyway.”
Then give them one that holds. The often-quoted IBM figure, by which every dollar spent on e-learning returns thirty in productivity, is a good conversation opener but no proof for your own organisation. More convincing is a small figure of your own: expert hours saved, multiplied by the hourly rate, against the cost of the training.
That calculation is unspectacular. Which is exactly why it gets believed.
“We do not need this for mandatory training.”
Not for proof; completion in the LMS, cleanly documented, is enough there, as described in SCORM explained. But mandatory training has a goal beyond proof: fewer incidents, fewer violations. Whether that goal is reached only an operational metric can show. How mandatory training can be adapted quickly enough for measurement results to have consequences is shown in Rolling out mandatory training in under 24 hours.
