Learning experience measurement

- Trying to create compelling learning experiences without measuring them, is like a music producer working in silence, it's simply not possible. So how do we ensure that we're listening to that feedback when it's available? Learning Experience Measurement defines what measures and data matter the most and then tracks that data. The most straightforward example is a score on an assessment, but there are so many other data points worth collecting to inform how someone experiences digital learning. Let's take a look at a learning program we're building for a company called Souder Manufacturing, where we've been tasked with training location managers on a new production management software. The learning experiences we created are time and effort intensive, so we need to pay attention to the variety of learning experiences we're providing and plan to measure each. Our dynamic learning experiences include online courses, videos, coaching and collaborative learning tools, so each must be measured in part. Let's look at the different questions around data points and various modalities to help us understand the learning experience. When are activities completed? How much time is being spent in each activity? Are they receiving passing scores or quitting early? If they do quit early, when do they quit? These metrics apply to activities such as elearning courses, videos and exams. How frequently do interventions occur? And what's the subject matter of each? What's the quality or rating of each coach? These metrics inform the quality of coaching experiences and other in-person interventions. What content is the most shared, or what are the forum topics that generate the most discussion? Whose comments are the most responded to? These measures help describe what's happening in a collaborative learning environment. As you being collecting and measuring data, knowing your data is key and it's important to understand what's missing and what's not needed. In order to check our data, we can ask key questions of the data's reliability, cleanliness and structure. Take the time to figure out where and why the data you're collecting doesn't match your expectations and then adjust as needed. Referring back to Souder Manufacturing's various learning modalities, let's consider the educational videos we have in many places of the training. Regardless of where someone watches a video, we need to consistently capture the same measures, like when they pause the video, or whether they watched it to completion. Keeping a consistent data model, makes it much easier to compare similar learning experiences that may have occurred at different times or in different places. Learning experience measurement can be so much more than scores and completions, and the more data points you can measure, the greater data set you'll have to draw further conclusions during future analysis. So think of a learning experience in your work and determine what events or interactions you want to know more about and use that to define an initial set of data points to measure.