If we can’t measure it should we still learn it?
A while back I wrote a blog post about learning outcomes/objectives or what I like to refer to as learning targets. As an instructional designer, I often get the feeling that folks either love or hate targets for learning. There rarely seems to be an in between. I don’t think the issue is whether or not learning targets are inherently good or bad. Instead it is more about asking how can we tweak them to fit different contexts. Say for example, the differences that exist between targeting learning in a humanities-related course versus in a science-related course. In both, we are most likely accountable for providing some evidence of progress to both a higher power as well as to the learners themselves. However, the humanities-based course may do that differently than how the science-based course does. This, of course, is perfectly okay and speaks to my point that targets are helpful in all areas as long as we modify them to work the way we need them to.
There are a number of ways to consider using targets for learning that move away from the traditional practice of driving evaluation and measurement. I mention some of them in the blog post I referenced above.
So how do you figure out how to get learning targets to work for you in an environment where the answer isn’t obvious or the steps to mastery aren’t necessarily clear? Outcomes-based assessment isn’t going to work for everyone in every context. One idea is to use a sense-making framework. A sense-making framework is different from an organizational framework. In an organizational framework you start with the model or framework and then you plug the data into it. With a sense-making framework you start with the data and the patterns of the model emerges from making sense of that data. Categorization is good for exploitation while sense-making is good for exploration.
The Cynefin Framework is a good sense-making tool for helping us better manage the distinction between learning that is more outcome-based and learning that is emergent. It is composed of four domains, two of which — simple and chaotic — don’t really apply to this story. The other two domains — complicated and complex — are what we could consider using for helping us make better sense of how to establish targets for learning in different contexts.
Things in the complicated domain may have multiple right answers but getting to these answers is an easier process than things in the complex domain.
Using the Cynefin Framework for measuring learning comes from Kristen Eshleman who was a guest on this Teaching in Higher Ed podcast as well as wrote this blog post on the topic.
Learning is messy on a good day. Realizing that sometimes that messiness can be organized and sometimes that messiness just has to be allowed to emerge as is is an important step towards developing a greater appreciation for the value of learning targets.
