Design-Driven Decision Making Starts With Mental Models
In our recently released Design and Data In Balance report we highlight the complementary, but distinct B-side to data-driven decision making - what we call design-driven decision making. Our report argues that data-driven decision making in and of itself is not sufficient to raise student achievement and that efforts to increase the design-driven decision making skills of those working in schools are necessary if we hope to improve them. This is not a debate about the importance of data-driven decision making. Rather, our attention to design-driven decision making reflects the belief that we are dealing with a different animal and we need to better understand it.
In a compelling article, John Shibley suggests that the reason why organizations often do not improve is because the people within them tend to “leap to action” - going from the observation of events to the implementation of solutions without fully understanding the mental models and structures producing those events. In other words, we unintentionally skip over the design-driven side of the work. There are good reasons why we do this. First, our frenetic workday coupled with the urgent need to support our students creates pressure to quickly solve problems. This often leads to fixes that fail. Second, we are not well equipped to surface, examine and question our mental models. Few in the field of education have been trained how to do this type of work well. But if our mental models are fuzzy and ill-defined, design efforts are unlikely to be effective.
Design-driven decision making starts with mental models so understanding what they are matters. Doyle and Ford have authored an article in which they define and unbundle the attributes of mental models. Below is my layman’s interpretation of their key points:
A mental model is a fairly coherent theory or set of beliefs that represents an external system. A mental model exists only in the mind, but it can be drawn out via conscious introspection. And while details within our mental model might change and be refined over time, the gestalt or wholeness of our mental models are harder to change and therefore, more likely to endure. Mental models are underspecified - that is, they are based on the information we have at hand which is almost always incomplete. So not only are mental models perception of the structures of an external system, they tend to underestimate the complexity of those systems.
Given this description of mental models, we can now identify strategies that will build up our design-driven decision making skills. The implications of Ford and Doyle’s definition:
Articulating mental models is neither quick nor easy. Therefore, an important precondition for cultivating effective design-driven decision making skills is having the time to surface mental models. In other words, we have to create space in schools that allow for introspection and we need to make good use of the time we do have. (To make better use of our time, see Joe McDonald’s The Power of Protocols).
We need tools (and training on how to use tools) that map our mental models. Systems thinking is a field of study that specifically supports the explication, operationalization and simulation of mental models. We have put in place [The Applied Systems Thinker](www.theappliedsystemsthinker.com), a website to help educators deepen their systems thinking skills.
If mental models endure as Doyle and Ford suggest, then challenging and then changing them is not going to be something that just happens. How can we support and speed up this process? By modeling a system using systems thinking tools like STELLA, we are able to step “outside the system” and “see it whole.” Simulations allow us to test our mental models and see the implications of our models unfold. Watching scenarios play out deepens our understanding of feedback loops and provides risk-free, low cost opportunities to learn. In so doing, our mental models evolve. And if we do this in a team context, where different people see the organization from different vantage points, our model will be more robust.
By starting with mental models, design-driven decision making is starting with people. This requires a different approach than if our starting point is data. Data-driven decision making without a strong design-driven counterpart is tantamount to circling the problem or walking along its perimeter. The design side invites us in. Dynamic systems thinking is one framework that supports design-driven decision making, but there are many frameworks and tools within the field of improvement science. Stay tuned for more information on the work that New Visions is doing to support continuous improvement in our schools.