Cut the Scrap with Predictive Learning Analytics™
What’s the #1 issue facing the L&D profession today?
It’s scrap learning —and if you haven’t heard the phrase yet, you will.
“Scrap learning,” a term coined by KnowledgeAdvisors, describes the wasteland of learning that is delivered, but not applied back on the job. It’s a critical business issue because it wastes critical money and time — precious organizational resources.
How big is the problem? Two benchmark research studies help put this in perspective. In 2004, Rob Brinkerhoff, professor at Western Michigan University, found that slightly less than 20% of learners never apply what they learn in a training program back on the job and another 65% try to apply what they learned, but revert back to their old ways for a whopping 80% scrap learning figure. More recently, a 2014 Conference Executive Board (CEB) white paper reported that in the average organization, 45% of all learning delivered ends up not being applied.
What does this look like at the individual organizational level? Two statistics from the Association for Talent Development (ATD) 2015 “State of the Industry Report” help bring this into sharp focus:
- Average per employee training expenditure and
- Average number of training hours consumed per employee.
In 2015, these two figures were $1229 USD and 32.4 hours, respectively.
Using the CEB 45% scrap learning figure you can see in the chart below that $553 USD of $2129 USD is wasted money and 14.6 of the 32.4 hours is wasted time. The picture is even more bleak using the Brinkerhoff 85% research which shows that $1045 USD of $1229 USD is wasted money and 27.5 of the 32.4 hours is wasted time.
Whether it’s 45% or 85%, think about the resources wasted in planning and delivering training and the lost opportunity from training not applied!
So what’s the solution?
The short answer is use Predictive Learning Analytics™ (PLA).
PLA is a new method for finding and quantifying hidden patterns in evaluation data in order to predict the future likelihood of certain individual learner behaviors and actions.
PLA is different from traditional learning program evaluation in two significant ways: 1) the focus is on the individual learner and not on programs or cohorts; and 2) the focus is on predicting future behaviors or actions and not on documenting what has already happened.
Given PLA’s ability to focus on unique individuals and peer into the future, imagine the possibilities if we could know at the conclusion of a learning program, which participants did and didn’t learn the material taught, and which ones are most and least likely to apply what they learned back on the job. All this and more is possible using PLA.
Want to learn more about how to incorporate PLA into your measurement and evaluation efforts? >> Come join me at an upcoming PLA Event!
Image Credit / Copyright: urric / 123RF Stock Photo