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Ideas Development

Asking the Right Questions

Don’t let years of wisdom and tacit knowledge vanish with the past. Capture it!

Last year, NogginLabs celebrated 20 years of eLearning that doesn’t make learners feel dead inside. How do we do it? Well, there’s the four pillars: instructional design, writing, graphic design, and programming. There’s the office full of smart, funny, and nice people. And honestly? Experience has taught us a lot.

Preserving a Legacy of Learning

But what happens when your employees have been trained? Perhaps you already have some really cool eLearning, and the behavior change you were looking for has not only manifested, but turned into something even better than you’d hoped. Perhaps those people you trained have taken those foundational behaviors and used them to advance your business even further, creating new ideas and processes that you couldn’t have trained them for because, well, they’re the sort of ideas you get from simply doing your job over a long period of time.

Or perhaps those successful trainees have become the sort of people your new employees count on. Every company has them. They’re the go-to people when there’s a problem. They’re the people who can keep their cool, even in the most stressful or unpredictable situations. While they give the impression that nothing surprises them, they truly relish the mysteries the job brings.

So what do you do when that person moves on? What happens to that dependable cool head, their wisdom, or the untrainable ingrained knowledge they gained?

With any luck, some of it has been documented somewhere, or your newer employees figure out their own way to do it. We tend to believe in the importance of preserving the history of ancient cultures, so why don’t we put much value in the legacy of company cultures? Why do we put so little value in preserving the lessons learned from those who came before us?

How to Capture a Brain

Early in 2017, one organization revealed that they were concerned about this problem. They  were worried that, as a group of their foundational employees approached retirement, their venerable organization might lose what they’d spent decades trying to build: NASA’s Jet Propulsion Laboratory at Caltech. This group of engineers and scientists had seen the growth of the space program first-hand; but slowly, they began to leave, taking decades of unique knowledge and experience with them. To make sure they didn’t lose any more of that knowledge, JPL turned to NogginLabs.

What started as a meeting about a potential eLearning project turned into something brand new for NogginLabs: We were going to capture and preserve the minds of some of the country’s brightest scientists in a process that would come to be known simply as “knowledge capture.”

“Person after person kept bringing up this major problem of, ‘If only there was a way that we could just capture…the brains and knowledge of the scientists that are leaving,” says NogginLabs founder Brian Knudson of his first meeting with JPL in Pasadena.

It just struck me as stunning that we were talking about anything else…. This is the only thing we should be doing.

With that enthusiasm, Brian soon found himself as the guinea pig for the first new content production process NogginLabs has seen in a while.

The Science of Learning

In a spring 2000 article in the Harvard Business Review, John Seely Brown and Paul Duguid wrote about the difference between re-engineering and knowledge management, in light of the explosion of the internet and its impact on business and management in the ‘90s. Though today we don’t give the internet’s role in business a second thought, their analysis remains relevant on a higher level.

Explaining the managerial shift from process engineering to knowledge management, they write, “It suggests a dilemma that all managers grapple with: the organizational tension between process, the way matters are formally organized, and practice, the way things actually get done.”

“The way things actually get done” is what we were chasing in our inaugural knowledge capture project.

So how do we get things done? Great question. While rocket science is not part of our repertoire, we like call upon another type of science around here: learning science.

What was different about our project is that we weren’t necessarily trying to find out what these people had done over the course of the years. While establishing those facts were important in informing our interviews with them, we wanted to know how they did those things, how they thought up the solutions no one else could think of, and why that was their thought process. So while we needed to learn enough about rocket science to ask those questions, we realized that wasn’t as big of an obstacle as it first seemed. Our first challenge was to figure out how to gather the knowledge behind the science in the first place.

Cognition Vs. True Knowledge

As Brian began his research on knowledge capture, the more he dug, the more he saw just how unique our endeavor was. He explains:

What I was looking for was whether there was some science behind knowledge capture. What we’re really good at is reading science that might have something just on the periphery to do with what we’re doing. And then, being able to apply those techniques to the weird things that we have to do.

After speaking with an expert from Northwestern University’s Learning Sciences department, Brian found a science that might work, or at least form a viable foundation from which we could develop our process. It was called Cognitive Task Analysis (CTA). The widely cited explanation of this process comes from the appropriately named book, Cognitive Task Analysis, by Jan Maarten Schraagen, Susan F. Chipman, and Valerie L. Shalin, in which they write, “[C]ognitive task analysis is the extension of traditional task analysis techniques to yield information about the knowledge, thought processes, and goal structures that underlie observable task performance.” This information covers both the “overt observable behavior” and the “covert cognitive functions” behind a task.

The great part about CTA is that it explores the parts of a task that you don’t think about. It’s like a chef’s mise en place—the way their ingredients and tools are arranged around them. Ask a chef why she keeps her tasting spoons to her left. The first answer is probably, “I don’t know.” But as she unpacks a procedure such as “making a sauce” and gets to the step where she tests the sauce, she realizes that the tasting spoons are where they are because they’re not as high a priority. Other ingredients, like salt, pepper, oil, herbs, and spices, need to be more centrally located. However, the location of the tasting spoons turns the chef back toward the counter where, if the sauce tastes right, she will add it to the dish. Each chef prefers things a little different, but those preferences are developed over time and become second nature.

The reason that wouldn’t totally work for our knowledge capture process is because, while the questions used in those analyses are useful, they’re usually focused on what a subject is doing. But that wasn’t quite what we needed, which was why they do it and why they do it the way they do. Most importantly, we needed to get into some personal histories to learn how they came up with their processes and conclusions.

It quickly became clear that we needed to make a trial run, to experiment with our process. So we got Brian into a room, set up some cameras, and asked him some questions.

Need a Custom Solution? Just ASK!

As we wrapped up our first day of practicing interviewing Brian, it became increasingly evident that CTA wasn’t a perfect solution. Brian recalls:

As soon as we did it it was like, ‘Oh my god, we’re missing all these things and all these parts.’ That’s why it’s a custom solution. That’s why we’re the only ones that have it.

This is where we not only got to practice more, but to study the art of interviewing. We experimented with techniques that might elicit enlightening responses, while creating a network that a person in a similar field as the expert’s would genuinely be interested in. This network is the first part of the process where we were able to call upon our own personal experience, knowledge, and expertise: with an ASK System.

ASK Systems have been around since the ‘90s, and Northwestern University’s Learning Sciences department (Brian’s alma mater) wrote on it extensively. Basically, an ASK System is a curated, analytics-driven collection of videos about a specific topic, organized in such a way that others can easily reference them to gain an insider’s view on a given topic.

“The original theory was that they could build a computer system that actually understood the videos, and then based on that, present to you what you most likely want to hear next,” says Brian. “So not only did [the computer] understand the videos… but it could make intelligent assumptions about what you might want to hear next.”

The trouble with these old ASK Systems is that they required an insane amount of time and money, as someone would have to manually input and connect keywords. Our system naturally follows the thought process of a curious human. The only way to create it is to ask our interview subjects great questions and to pursue our natural curiosities.

When using an ASK System like this—one that is based on learning science and the way our minds work—it can be easy to view knowledge capture as a pretty simple idea. But think about it. Why does it feel simple? Is it because the ASK System is easy to use? Is it because the videos suggested to you actually focus on what you were wondering about after you viewed the one before? Are they the natural follow-ups that you might wish you could ask a mentor, even after they’ve moved on?

That feeling is another part of what makes our knowledge capture process and ASK System so unique and, as cheesy as it sounds, special. It’s a combination of time, expertise, and thought that you really can’t achieve without effort and care. The fact that it feels easy and simple is because it’s designed graphically and instructionally well. That makes for a good user experience, and it’s how every NogginLabs project should feel. It is truly a custom solution.

So How do You Know what to Ask?

In the end, this strange combination of our own expertise, advanced learning science, a study in journalism, and an insatiable curiosity created this process that we brought to JPL. And the results were everything we’d hoped for. JPL’s mission was about preserving decades of incredibly tedious, challenging, and heartfelt work. They wanted to preserve it so that they could share their understanding of their specific fields, which will lead to a greater understanding of humankind’s place in the universe.

For NogginLabs, and Old Man Noggin (Brian’s self-given monicker, I promise), the knowledge capture process has also become a way for us to preserve the spirit of our quirky, dedicated eLearning company. It’s about appreciating that a problem is just an opportunity to find a new, cool solution and to prove that there is another, more fun way to do things. It’s understanding that while we don’t have every answer right away, we can always figure it out.

“It’s hard sometimes for us to accept as a little company that we are really the only people doing this in the world,” says Brian. “There are people capturing things and there are journalists who have great interviews with some of these scientists, but their intent is not to do what we’re doing, nor is their output going to be what we have.”

There are people capturing articles, but nobody’s doing this, because we’re the only people in the world that are willing to tackle this and think that it can be done.

“It confirmed many things I thought,” he continues. “It confirmed my worldview, which is like, people just aren’t asking the right questions. You know, and I couldn’t tell you what those questions are until this project. Or how to go about finding the right questions, I can just tell you we weren’t asking the right questions.”

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