36 min
Building Knowledge Through Predictions: Deming in Schools Case Study with John Dues (Part 4) In Their Own Words
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- Management
In this episode (part 4 of the series), John and Andrew continue their discussion from part 3. They talk about how to use data charting in combination with the Plan-Do-Study-Act cycle to gain the knowledge managers need to lead effectively.
0:00:00.1 Andrew Stotz: My name is Andrew Stotz, and I'll be your host as we continue our journey into the teachings of Dr. W. Edwards Deming. Today I am continuing my discussion with John Dues, who is part of the new generation of educators striving to apply Dr. Deming's principles to unleash student joy in learning. The topic for today is Prediction is a Measure of Knowledge. And John, to you and the listeners, I have to apologize. I'm a bit froggy today, but John, take it away.
0:00:30.9 John Dues: Yeah, Andrew, it's great to be back. I thought what we could do is sort of build off, what we were talking about in the last episode. We sort of left off with sort of an introduction to process behavior charts and importance of charting your data over time. And sort of the idea this time is that, like you said at the outset is prediction is a measure of knowledge and prediction is a big part of improvement. So I thought we'd get into that. What role prediction plays in improvement, how it factors in and how we can use our chart in combination with another powerful tool, the Plan-Do-Study-Act cycle to bring about improvement in our organizations.
0:01:15.1 AS: And when you say that prediction is a measure of knowledge, you're saying that prediction is a measure of how much you know about a system, or how would you describe that in more simple terms that for someone who may not understand that, that they could understand?
0:01:31.4 JD: Yeah, it took me a while to understand this. I think, basically the accuracy of your prediction about any system or process is an observable measure of knowledge. So when you can make a prediction about how a system or a process, and I use those words interchangeably, is gonna perform the closer that that sort of initial theory is, that initial prediction is to what actually happens in reality, the more you know about that system or process. So when I say prediction is a measure of knowledge, that's what I'm talking about is, you make a prediction about how something's gonna perform. The closer that prediction is to how it actually performs, the more you know about that system or process.
0:02:19.1 AS: I was just thinking about a parent who understands their kid very well can oftentimes predict their response to a situation. But if you brought a new kid into that house that the parent didn't know anything about their history, their background, the way they react, that the parent doesn't really have anything to go on to predict except maybe general knowledge of kids and specific knowledge of their own kid. How could that relate to what you're saying that prediction is a measure of knowledge?
0:02:52.3 JD: Well, I think that's a great analogy. One of the things that Dr. Deming said that it took me some time to understand was that knowledge has temporal spread - just a few words, but really causes some deep thinking. And I think what he meant was, your understanding, your knowledge of some topic or system or process or your kid has temporal spread. So that understanding sort of increases as you have increased interaction with that system process or in this analogy, your own kid. So when you replace a parent who knows their kid well with some other person that doesn't know that kid as well, they haven't had that sort of, that that same, that shared time together. So there's that, they don't have that same understanding. It's gonna take time for that understanding to build. I think the same thing happens when we're trying to change a system or a process or improve it or implementing a new idea in our system or process. And so the prediction at the outset is probably gonna be off. Right, and then over time, hopefully as we learn about that
In this episode (part 4 of the series), John and Andrew continue their discussion from part 3. They talk about how to use data charting in combination with the Plan-Do-Study-Act cycle to gain the knowledge managers need to lead effectively.
0:00:00.1 Andrew Stotz: My name is Andrew Stotz, and I'll be your host as we continue our journey into the teachings of Dr. W. Edwards Deming. Today I am continuing my discussion with John Dues, who is part of the new generation of educators striving to apply Dr. Deming's principles to unleash student joy in learning. The topic for today is Prediction is a Measure of Knowledge. And John, to you and the listeners, I have to apologize. I'm a bit froggy today, but John, take it away.
0:00:30.9 John Dues: Yeah, Andrew, it's great to be back. I thought what we could do is sort of build off, what we were talking about in the last episode. We sort of left off with sort of an introduction to process behavior charts and importance of charting your data over time. And sort of the idea this time is that, like you said at the outset is prediction is a measure of knowledge and prediction is a big part of improvement. So I thought we'd get into that. What role prediction plays in improvement, how it factors in and how we can use our chart in combination with another powerful tool, the Plan-Do-Study-Act cycle to bring about improvement in our organizations.
0:01:15.1 AS: And when you say that prediction is a measure of knowledge, you're saying that prediction is a measure of how much you know about a system, or how would you describe that in more simple terms that for someone who may not understand that, that they could understand?
0:01:31.4 JD: Yeah, it took me a while to understand this. I think, basically the accuracy of your prediction about any system or process is an observable measure of knowledge. So when you can make a prediction about how a system or a process, and I use those words interchangeably, is gonna perform the closer that that sort of initial theory is, that initial prediction is to what actually happens in reality, the more you know about that system or process. So when I say prediction is a measure of knowledge, that's what I'm talking about is, you make a prediction about how something's gonna perform. The closer that prediction is to how it actually performs, the more you know about that system or process.
0:02:19.1 AS: I was just thinking about a parent who understands their kid very well can oftentimes predict their response to a situation. But if you brought a new kid into that house that the parent didn't know anything about their history, their background, the way they react, that the parent doesn't really have anything to go on to predict except maybe general knowledge of kids and specific knowledge of their own kid. How could that relate to what you're saying that prediction is a measure of knowledge?
0:02:52.3 JD: Well, I think that's a great analogy. One of the things that Dr. Deming said that it took me some time to understand was that knowledge has temporal spread - just a few words, but really causes some deep thinking. And I think what he meant was, your understanding, your knowledge of some topic or system or process or your kid has temporal spread. So that understanding sort of increases as you have increased interaction with that system process or in this analogy, your own kid. So when you replace a parent who knows their kid well with some other person that doesn't know that kid as well, they haven't had that sort of, that that same, that shared time together. So there's that, they don't have that same understanding. It's gonna take time for that understanding to build. I think the same thing happens when we're trying to change a system or a process or improve it or implementing a new idea in our system or process. And so the prediction at the outset is probably gonna be off. Right, and then over time, hopefully as we learn about that
36 min