Rigorous Learning Methodologies

Rigorous Learning Methodologies:

Tame Problems: These kinds of problems usually have answers, or if not, they have countermeasures — actions that will put a system in a better state. However, they are not complicated by people having greatly differing world views or differing fundamental assumptions about how things work, or ought to work. Problem solvers can agree to criteria for deciding what is a fact.

Tame problems can usually be resolved by disciplined use of some form of PDCA logic: ask 5 or more whys; go to the gemba and see; gather and organize data; then think both critically and imaginatively. For example, finding the cause of flaws coming out of a factory’s paint system may require an extensive fishbone diagram to map, and some of the bones may not be defined at first, but in time, persistent people are likely to turn up causes and devise countermeasures. 

Wicked Problems:” Here systems are always in flux, always evolving, and often, involving human conflict. They may feature a clash in money interests, world views, or concepts of ethical behavior. A solution for one is an abomination to others. People may not even agree that a problem exists, or if there is one, prefer to hide it and not address it. (Deciding to make human systems more symbiotic with nature is bound to dredge up such situations.)

Wicked problems are as much behavioral and emotional as logical. If they can be resolved (or even dissolved — made to disappear), it is more likely to happen through insightful dialog. Dialog is under Behavior for Learning

A common issue with both tame and wicked problems is not studying a problem deeply enough frame it in context. Asking a good question can take one a long way down a path to resolving a problem.  

Rigorous Record Systems: If we can’t recall what we learn at the right time, we are apt to rework the same problem, or a similar one time and again. However, recording what we learn so that it can be shared and used by others is not as easy as it sounds. We have to record our understanding of the problem at the time, what was done and why; then record the outcome. For learning, failures are as important to record as successes. (Didn’t work kids, and here’s why.) But if conditions change, an idea that did not work the first time might work later. 

Reporting to a record system requires us to recap what was done and why, and just this refreshment reinforces learning. We have to codify learning, not just do a fix and move on. Organizing our logic by an A3 paper format is an example. No problem or project is complete until it is recorded in the record system. Human psychology is a factor; we do not like to report negatives or failures in detail, but that is important learning. That’s why leaders for learning have to emphasize learning, not just successful problem resolutions. 

A record of what happened is useless if we can’t retrieve relevant information when new situations are faced. A filing system need not be complex; it’s better if it is simple. Computerized knowledge management systems should make this less clerically onerous than in paper-laden days of yore. 

To explain why design and development of a record system is important, consider how the importance of a university library is explained to graduate students learning the history of their fields. “The library is our past speaking to our present so that you can make our future better than today.”