Learning from cases; getting smarter by design

One of my enduring memories from my first days in business school is a video interview of a second year student offering advice on surviving the case method. While I didn’t fully appreciate it at the time, it was totally appropriate that his advice was a case study in its own right.

He began with a story of the business challenge of “sexing chickens.” In the chicken business, it turns out that it is important to separate roosters and hens when both are no more than puffs of feathers. The clues are subtle and not reducible to a checklist. Novice chicken sexers can’t be taught to do their job but they can learn.

Prospective chicken sexers go through an apprenticeship. They sit down with a batch of chicks, pick one up, flip it over, inspect, and guess. Sitting next to our prospect is a veteran chicken sexer. The veteran’s job is to give the prospect feedback; either a “yes” for a correct guess or a smack in the head for a mistake. After a hundred or so guesses, the prospect’s error rate will drop close to zero. Neither the prospect or the veteran will be able to offer an explicit theory of what they are doing, yet they are effective.

Learning by the case method is a similar process of guessing and rapid, memorable, feedback. As an aside, recognize that this also describes the essence of machine learning. It is experiential learning at its purest.

The craft in designing case method learning lies in selecting and sequencing cases so that the lessons can be delivered more rapidly and reliably than the random accumulation of experience permits. The assumption here is that there is an order that can be exploited to guide action. There must be an underlying pattern that one can solve for.

If you subscribe to the value of the case method as a learning strategy, you are making a claim that there is a balance between theory and practice to be managed. It is a claim that the particulars matter; that experience or theory can only go so far in crafting a response. That you have a responsibility to design a response that acknowledges that every situation is a mix of old and new, predictable and unpredictable.

There’s a notion here that I am struggling with that has to do with the rate of change. I think that case-based learning potentially makes this issue more visible.

In a slow-changing world, experience matters greatly. Recognize how this situation maps to what we’ve seen and responded to before and right action is clear. As the rate of change increases, the value of experience changes. Prior experiences suggest actions and responses that can serve as the basis for designing a modified response that blends old and new.

Pushing the rate of change still higher means that effective response demands more design and less “here’s what we’ve done before.” What does that imply for learning effectively?

My hypothesis is that we need to make the experiential learning process visible, explicit, and deliberate. In a conventional case-based learning environment, there is a separation between those who are learning and those who are facilitating the learning—which is not the same thing as teaching. The goal is for those learning to build a robust, internal, theory of action. The facilitators have strong ideas about what that theory should look like and cases are sequenced to force the learners to develop an internal theory that matches the target theory.

What’s happening in higher-paced environments is that our learners and facilitators are becoming harder to distinguish. In a sense, we are back to a world of pure learning from experience. What changes is that as learners we now must be responsible for building our theories dynamically.

While this can still be described as a form of reflective practice, that reflection now must operate at several levels of abstraction. We can’t rely simply on organic processes to slowly and unpredictably get smarter over time. We need to get smarter on purpose and by design.

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