Have you ever wondered if learning feedback systems are measuring the wrong things at the wrong time through the wrong channels? We ask about satisfaction after sessions end, assess knowledge weeks after content delivery (for instructor-led training), and evaluate application performance months later, when it’s more challenging to make improvements.
Meanwhile, learners send us immediate, actionable feedback signals constantly. The problem isn’t that we lack feedback from our learners, it’s that feedback loops are structured mroe like post-mortems than navigation systems.
What Direct Feedback Actually Looks Like
At six weeks into recovering from foot surgery, I’m learning what direct feedback systems look like. Physical limitations create immediate, unfiltered information about what’s working and what isn’t. Pain signals problems instantly. Energy levels indicate capacity in real-time. Range of motion shows progress directly. This feedback isn’t delayed or filtered through satisfaction surveys. It’s connected immediately to decisions that matter, and it informs the next action rather than evaluating the last one.
Learning design rarely operates this way. In my post about completion rates, I explored what we should measure beyond compliance metrics. But recovery is revealing a deeper issue: It’s not just what we measure, but how disconnected our feedback loops are from the learning process itself. (For an alternative explanation of feedback loops take a look at this post by Terry Heick.)
The Communication Delay Problem
Research on virtual instructor-led training effectiveness shows that systematic design approaches outperform technology features. But systematic design depends on feedback loops that can actually inform design decisions. Yet most learning feedback comes too late to improve the experience. We measure engagement after sessions end, assess comprehension days or weeks after content delivery, and track application months later. By the time we get usable information, the learning context has passed.
The rub – learners provide immediate feedback constantly. The hesitation before answering questions. How they rephrase concepts back to us. Whether they return to specific resources or bookmark particular sections. These are real-time signals about what is or isn’t working. The question here is: Have we designed to capture and respond?
Beyond Satisfaction: Performance-Connected Feedback
The indicators I identified in earlier posts (voluntary re-engagement, sophisticated questioning, cross-contextual application) are all direct performance signals. So how do we build systems that respond to them during learning? The feedback my foot is giving to me is immediate and connected to outcomes that matter. Learning feedback should also be connected this directly to learning outcomes. Let’s measure retention instead of reaction, skill transfer instead of satisfaction.
The Social Acceptability Filter
Sounds simple, so what’s stopping us? One barrier to direct learning feedback is social acceptability. Physical feedback doesn’t care about politeness. Pain tells you to stop regardless of social considerations. (Even though I was embarrassed to be stuck on the ground in public a half mile from my office when I broke my foot, I wasn’t going to stand up.) But in learning environments, social filters can make honest feedback feel risky.
People say they “got it” when they’re confused. They may rate sessions positively to avoid seeming difficult or nod along when they’re lost because questioning feels like admitting inadequacy. (I mean, we’ve all been there.) But what if we designed learning environments where direct feedback felt as natural as physical feedback? Where confusion was treated as valuable naviagation data rather than failure.
Building Learning Feedback Systems: Navigation Systems Instead of Evaluation Systems
Consider a feedback system that functions like navigation tools. Tools that help us adjust course while traveling, not evaluate the trip after you’ve arrived. (And let’s please not mention building the airplane while flying. Maybe I’ll share my thoughts on that another time.) Instead of post-session surveys, we could build in real-time comprehension checks that actually inform content delivery. Instead of delayed knowledge assessments, we could track application attempts that reveal gaps while there’s still time to address them. This isn’t about more technology or complex analytics. It’s about designing feedback loops that connect directly to learning decisions and respond quickly enough to improve the experience while it’s happening.
The goal isn’t perfect measurement. Don’t get me wrong. I still value the Kirkpatrick levels and they have their place. But we need to build feedback systems that actually improve learning while there’s still time to make adjustments. What direct learning signals have you noticed that current systems typically ignore? When you’ve experienced learning environments with good feedback loops, what made them different? I’d love to hear about the gaps you’ve observed and what signals you trust instead. Share your thoughts at the Coffee Corner or on LinkedIn.
