In the final episode of the goal setting in classrooms series, John Dues and Andrew Stotz discuss the last three of the 10 Key Lessons for implementing Deming in schools. They finish up with the example of Jessica's 4th-grade science class. TRANSCRIPT 0:00:02.4 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'm 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. This is episode six about goal setting through a Deming lens. John, take it away. 0:00:26.4 John Dues: Hey, Andrew, it's good to be back. Yeah, for the past handful of episodes or so, we've been talking about organizational goal setting. We covered these four conditions of healthy goal setting and then got into these 10 key lessons for data analysis. And then we've been looking at those 10 key lessons applied to an improvement project. And we've been talking about a project that was completed by Jessica Cutler and she did a Continual Improvement Fellowship with us here at our schools. And if you remember, Jessica was attempting to improve the joy in learning of her students in her fourth grade science class. So last time we looked at lessons five through seven. Today we're gonna look at those final three lessons, eight, nine and ten applied to her project. 0:01:15.7 AS: It's exciting. 0:01:17.1 JD: Yeah. So we'll jump in here. We'll kind of do a description, a refresher of each lesson. And we'll kind of talk about how it was applied to her specific project, and we'll look at some of her data to kind of bring that live for those of the folks that have video. Let's jump in with lesson number eight. So we've talked about this before, but lesson number eight was: more timely data is better for improvement purposes. So we've talked about this a lot. We've talked about something like state testing data. We've said, it can be useful, but it's not super useful for improvement purposes, because we don't get it until the year ends. And students in our case, have already gone on summer vacation by the time that data comes in. And you know that the analogous data probably happens in lots of different sectors where you get data that lags, to the point that it's not really that useful for improvement purposes. 0:02:15.8 JD: So when we're trying to improve something, more frequent data is helpful because then we can sort of see if an intervention that we're trying is having an effect, the intended effect. We can learn that more quickly if we have more frequent data. And so it's, there's not a hard and fast rule, I don't think for how frequently you should be gathering data. It just sort of needs to be in sync with the improvement context. I think that's the important thing. Whether it's daily or a couple times a day or weekly, or monthly, quarterly, whatever, it's gotta be in sync with whatever you're trying to improve. 0:02:50.5 AS: You made me think about a documentary I saw about, how they do brain surgery and how the patient can't be sedated because they're asking the patient questions about, do you feel this and they're testing whether they're getting... They're trying to, let's say, get rid of a piece of a cancerous growth, and they wanna make sure that they're not getting into an area that's gonna damage their brain. And so, the feedback mechanism that they're getting through their tools and the feedback from the patient, it's horrifying to think of the whole thing. 0:03:27.7 JD: Yeah. 0:03:28.3 AS: It's a perfect example of why more timely data is useful for improvement purposes 'cause imagine if you didn't have that information, you knock the patient out, you get the cancerous growth, but who knows what you get in addition to that. 0:03:43.7 JD: Yeah, that's really interesting. I think that's certainly an extreme example, [laughter], but I think it's relevant. No matter what our context, that data allows us to understand what's going on, variation, trends, whether our system is stable, unstable, how we should go about improving. So it's not dissimilar from the doctors in that example. 0:04:06.8 AS: And it's indisputable I think, I would argue. But yet many people may not, they may be operating with data that's not timely. And so this is a reminder that we would pretty much always want that timely data. So that's lesson eight. Wow. 0:04:22.6 JD: Lesson eight. Yeah. And let's see how we can, I'll put a visualization on the screen so you can see what Jessica's data look like. All right. So now you can see. We've looked at these charts before. This is Jessica's process behavior chart for joy in science. So just to reorient, you have the joy percentage that students are feeling after a lesson on the x-axis, sorry, on the y-axis. On the x-axis, you have the school dates where they've collected this survey information from ...
