A while back I wrote about 8 transferable lessons from my Fitbit that I’ve applied to my L&D practice. As part of that post, I complained that the Fitbit sometimes gave me data, but I couldn’t do anything with it. Specifically, I was talking about my sleep pattern.
A typical night could look like this:
FORTY-ONE TIMES RESTLESS! That’s a lot of restlessness. It’s not good. But what am I supposed to do about it? It reminded me of my post-training evaluation scores.
Sometimes learners would give my sessions an average of 4.2. And sometimes those same learners would give a colleague’s presentation an average of 4.1 or 4.3 (even though I knew in my heart of hearts that my presentation was more engaging!!). But what could I do with these post-training evaluation scores? I’ll come back to this point in a minute.
As for my restlessness, my wife suggested something and suddenly my Fitbit sleep tracker looked a lot different. An app called Insight Timer. She used it one evening and I woke up the next morning to find the number of restless periods had decreased by 88%.
If there was hope for me to find value in my Fitbit sleep tracker, perhaps there was value in some of life’s other perplexing metrics… maybe even value in post-training evaluation scores!
Just as my wife offered an idea on how to make my Fitbit sleep metrics more actionable, Will Thalheimer helped me to reframe my attitude on post-training evaluation and identify ways to make those metrics more actionable.
In his book Performance-focused Smile Sheets: A Radical Re-thinking of a Dangerous Artform, he advocates for scrapping the traditional Likert-scale questions that currently fill up post-training evaluation scores, and instead ask questions like this:
In the past, I may have asked a Likert-style question about whether people agreed or disagreed (or strongly agreed or strongly disagreed) with the idea that their supervisor would support them in applying skills after a training session. And I’d probably get some sort of numeric score, like 4.2. But what does that mean?
With this new format, I get responses that breakdown like this:
Now I have a much better idea of how people feel they’ll be supported by their supervisor after a training session. This data is much more actionable than finding an average score of 4.2.
One thing I might want to do is follow-up with the client and offer them some suggestions of ways that supervisors could better support their staff in implementing ideas, knowledge, and skills from this workshop.
This data also tells me that I may need to adjust my design practices in the future to include a checklist of discussion points for learners to take from the session and use in one-on-one check-ins with their supervisors in order to make sure there is a post-training conversation around ideas, knowledge, and skills gained from this workshop.
If we’re going to collect data, it ought to be actionable… otherwise, we’re just collecting vanity metrics that may serve to make us feel good on the surface, but don’t really mean anything.
How are you using post-training evaluation? What are some metrics or data from your training programs that you wish could be more actionable?
