“Hey, what happened to that instrument problem? What did you try? Did it work?”
“I’ll cover you. No worries.”
Often, when visiting various client organizations, I pick up bits and pieces of conversations between coworkers. The snippets of conversation sometimes seem like Tweets or verbal Emoji—and often sound like some hidden code known only to the workers.
They got me wondering:
- How do workers think, work, and learn in the workflow?
- What do workers actually think and do?
- How can I synthesize my discoveries into a repeatable method?
To gain insight into how workers’ conversations contribute to workflow learning, I captured some of the common conversations and mapped them onto my Workflow Diagnostic Process, which is described in detail in “ ‘Figure it Out!’—A Practical Framework for Workflow Learning.”
Figure 1 shows the steps in the process.
Figure 1: The Workflow Diagnostic Process shows how people diagnose problems, think up and try solutions, and apply feedback.
Mapping conversations to the Workflow Diagnostic Process
1. Diagnosing problems
Conversations around diagnosing problems might start with these questions:
- What’s wrong with it?
- Where did it start?
- How bad is it?
- What’s the likely cause?
- What are the impacts?
- What happens if this is or isn’t fixed?
- Is it even worth fixing?
2. Finding answers and solutions
Once the problem has been identified, the questions and suggestions get a bit more specific, to tease out potential solutions:
- Ask Ben; he's worked on this before.
- What do I already know about this?
- I recall this to be the case.
- Is it the same as the other time?
- Check the logs and the suppliers.
3. Trial-and-error and testing
Multiple rounds of testing solutions might ensue:
- What happened?
- Did you try my suggestion?
- What did you discover?
- What did you try differently?
- How many times did you test it?
- How did you test it?
- Try it again—but do it slightly differently.
4. Metrics and feedback
Each solution might be evaluated with questions like these:
- Did it work?
- Is there still a problem?
- Did you compare it to other similar batches?
- I wonder if this affects the downstream or upstream parts?
- What do the data say?
As you can see, conversations follow patterns within the framework—which I developed using information I learned from workplace conversations.
In The Recursive Mind, Michael Corballis, a psychologist who studies how people use language, suggests, “Conversations are the forms of language, and language is our way of communicating our thoughts and behaviors. Conversations tell us what workers think and do while at work.”
How do workers work, think, and learn?
Conversations tell us that workers think, work, and learn all at the same time. The flow of conversations is natural and organic within the worker’s environment. At the center of these conversations are work issues. The conversations address real-world impacts on results in the workplace. Learning is highly significant at these times because it entails critical thinking within a real-world context.
What do workers actually think and do?
Borrowing from the works of Jane Bozarth and others, I developed a mental model to help answer the question: “What do workers actually think and do?”
By transposing conversations into characteristics of workflow learning and rating them, we’re able to observe how workers simultaneously think, work, and learn.
A look at workflow learning characteristics and the quality of conversations suggests a correlation between the depth of conversations and their impact on work results. To use conversations as indicators for progress in workflow learning (to fix, solve, and improve work issues), one can rate the quality of conversations from low to high. A low-quality conversation indicates one that is less likely to lead to results; a high-quality conversation is more likely to lead to results.
Workflow learning characteristics and quality of conversations
These sample conversations illustrate how workflow learning characteristics can apply to conversations.
Focus on work issues: direct work issues and impacts:
- “What are the impacts?”
- “What happens if this is/is not fixed?”
Learning from others: trusting others for reliable answers:
- “Ask Ben; he’s worked on this before.”
- “I recall this to be the case.”
- “Check the logs or suppliers.”
Sharing of work: share one’s solutions, methods, materials:
- “Is it the same as?”
- “Was there a similar incident?”
- “Try it again but do it slightly differently.”
Experimentation: continuous testing and discoveries, discover hidden aspects:
- “What did you discover?”
- “How many times did you test it?”
- “What did you try differently?”
Big picture thinking: going beyond one’s silo; gaining deeper knowledge:
- “Ask Ben. He’s worked on this before.”
- “Try it again but do it slightly differently.”
- “I wonder if this affects the downstream or upstream parts?”
A significant discovery is: When workers have conversations about real work issues and problems, the quality of conversations becomes deeper. The more artificial the work scenarios are, the lower the quality of the conversations.
Real-work issues trigger deeper conversations
I decided to test this hypothesis. From my workshops, I compared two data sets on what happens to the quality of conversations based on artificial scenarios vs. those pertaining to real-work issues.
Figure 2: Shows how participants responded to an exercise where work issues were made up by an instructional designer; between 15 percent and 38 percent found them helpful.
Figure 3: Shows how participants responded to an exercise when they selected work situations representing their actual, current concerns. Between 60 percent and 95 percent found these discussions helpful.
The survey data confirmed what experts tell us: Work is one of the best places to learn, and learners respond far better to realistic examples than fictional ones.
Of greater significance is that workflow learning offers a way to provide learning and support in conditions where real work issues are being fixed, solved, and improved by workers.
Devising a repeatable framework for workflow learning
The following principles and practices reinforce the workflow learning process:
- Allow workers and workshop participants to select real-world work issues.
- Let them go through the thinking process outlined in the Workflow Diagnostic Process.
- Use their conversations and self-ratings as a reflection process, with the goal of adding depth and maturity to their conversations and critical thinking skills.
- Collect data on workers’ experience within the Workflow Diagnostic Process.
Conversations bring workflow learning to life. Digital collaboration and learning technologies accelerate the conversations between workers. Conversations contain layers of stories and experience-sharing that make them even more engaging. Conversations set the context for workflow learning. Observing conversations is a window into how workers progress through the Workflow Diagnostic Process.
Master the workflow learning process
Learn how to implement the Workflow Diagnostic Process and turn conversations among colleagues into learning opportunities. Register today for “Boots on the Ground—Implementing Workflow Learning,” a daylong workshop presented by Ray Jimenez, PhD. This pre-conference workshop takes place October 22, ahead of DevLearn 2019 Conference & Expo, October 23–25 in Las Vegas.
Bersin, J. (2018, June 3). A New Paradigm for Corporate Learning: Learning in the Flow of Work.
Bozarth, J. (2014). Show Your Work. San Francisco: Wiley.
Corballis, M. (2014). The Recursive Mind: The Origins of Human Language, Thought, and Civilization. Princeton University Press.
Hagel, J. (2007, December 31). Mastering the Learning Pyramid.
Jarche, H. (2010, August 5). The Evolving Social Organization.
Jimenez, R. (2019). Workflow Learning. Monogatari.
Kahneman, D. (2013). Thinking, Fast and Slow. Farrar, Straus, and Giroux.