Podcast: Chad Dorsey on Modeling and Data Science in STEM Education

We’re listening to the Getting Smart podcast where we unpack what is new and innovative in education. I’m your host Jessica and today we’re talking about modeling and data science in STEM education with Chad Dorsey. Chad grew up a science geek.

He loved the lakes and meadows of summer camp. He studied physics in college and did doctoral work in geophysics at Oregon. He taught science in Maine and Vermont before taking on the leadership of the Concord Consortium. For more than a decade, Chad has been leading their effort to use tech to transform STEM education by empowering learners to ask and answer their own questions.

On today’s episode, Chad talks with Tom about the math education that all young people need. It looks like less calculating and more modeling, fewer small problems and more data science. Let’s listen in as Chad Dorsey talks about students being active agents of discovery. Chad Dorsey, welcome to the Getting Smart podcast. Thanks so much.

I’m very happy to be here. It’s great to have you live at the farm. It’s a wonderful place. I enjoy looking outside. Chad, what made you a science geek?

Boy, I think I was a science geek from, if not the very beginning, very close to it. My father was a science teacher. My mother was also an elementary school teacher. I grew up spending every summer of my life until age 13 at the Audubon Camp in Wisconsin on an 800 acre plot that had a chain of lakes, a forest, a meadow.

I lived in a log cabin from the 1800s. We went to people who were talking about nature and science and thinking about it. It just seemed like the way that you did things. I’ve loved it from the beginning. You went to college and studied physics.

Why physics? It was interesting, actually, because I wanted to study chemistry. Physics required four years, chemistry three. I had to start with physics and kept on going with chemistry for a while and then dropped it off and stayed on with physics.

Both of them were interesting. I’m fascinated by how the world works. Physics is about as close to that as you get. I was in hooked. How’d you get to Oregon?

You did a master’s at Oregon? I did a master’s at the University of Oregon. Actually, I finished my doctoral work, but took the knowledge and ran before I finished the doctorate degree because I got interested in teaching, which I’d been interested in all my life, but ended up in Oregon because I was looking for someplace different.

They had a great physics department that was available and I hadn’t been out to the Northwest. One thing led to another and I ended up in Oregon. Why geophysics? What do you… The advisor that I ended up with was one who studied volcanoes but actually studied it

in the lab in fascinating ways. He studied the way bubbles form. I got interested in geophysics in undergrad because of some research that I had done on glaciers and the like. I dragged a radar slide across glaciers to map the underbed and got to use lots of technology

in the early days of satellites. Then in graduate school, got interested in the kinds of physical experiments people were doing. They poured five-gallon buckets of corn syrup into giant glass containers and heated them from the bottom and watched the convection happening.

It was about as fun as you could get in a physical lab. I went to Colorado School of Mines 40 years ago to be an arctic and glaciological scientist. I had no idea. That’s wonderful. Then I found out I was susceptible to frostbite and my wife told me she was not going to live

on a glacier. Yes, there are those sides of things. I got interested in the geology side because all these fun people were doing things outside. I said, hey, science can be like that. That’s great.

When you decided to teach, you moved to the other side of the country. How’d you get a job in Vermont teaching? I was actually teaching in Maine. I flew out to Maine and interviewed for a couple jobs. I got a couple offers.

We drove cross-country from Oregon to Maine with our car on the back of a rider truck. We went on a wing and a prayer, just interested in seeing another part of the country. My wife, or what was to become my wife, my soon-to-be-fiance was I had a master’s in journalism. I had a master’s in teaching.

You could pretty much put your finger down on a map and we essentially did and ended up in Maine looking for birds on the other side of the country. I’m a birder as well. About 11 years ago, you had a chance to take over the leadership of the Concord Consortium. What interested you about that role?

I always say that I’m a physicist by training and educated by vocation and a geek by nature. When I was working before the Concord Consortium at the Maine Mathematics and Science Alliance doing professional development and technology-based assessment work, I discovered the work that Concord was doing and instantly recognized as kindred spirits in the kind of modeling and simulation and the work that I had been doing in physics graduate school but applied

for learning and the kinds of things that I wanted to do when I was teaching but wasn’t quite available. When the job became available, I jumped at it and had to be pinched and coaxed in for a minute to when I realized it was the CEO job but it’s really the kind of thing that I’ve always wanted.

What’s the origin story of Concord? So it’s a fascinating story in that the Concord Consortium isn’t really a consortium per se. It was a group of organizations that Bob Tinker sat down with at a coffee clutch in Concord, Massachusetts when he decided he wanted to move on from Turk, another nonprofit he had essentially grown from very small to 100-some people.

He looked around and realized it wasn’t a startup anymore and wanted to do something new and the group around the coffee table all had lots of different ideas. Bob was the one who could get grants and he got an NSF grant right away and Concord started from there and so the Consortium really never came to be except for the group of people that was the group that led by Bob.

But he was always interested from the beginning, even before founding the Concord Consortium decades before, in how technology could really change the way science, math, and engineering were taught and bring something fundamentally new to the table. He was doing it back in the day when technology barely was when there was this thing called an analog digital converter and sensors were being used in industry.

He was proselytizing about how you could change the way students did the labs that they were doing in a day and do them in minutes. And that whole sense came across from the beginning. People started to play around and connect different things to different computers and realized that there was a lot to be had in this kind of learning fostered by technology.

That same spirit I think has been there all along in a lot of ways. We’ve been doing the same thing with new technologies and old technologies all the way through. Now it’s just much more possible in many more places than it was back then. How do you think about the mission of Concord these days? So we think a lot about really what it means to open up learning.

We are all focused on STEM education, focused on what technology can do to transform STEM education. And the core, however, we’re really about empowering learners to realize that they can learn. We did some thinking about this a while back and recognized that in lots of ways STEM education

is a vehicle for the kind of learning that we all love to do. And really what’s powerful about learning science and math and engineering is that when you learn how to ask and answer your own questions, you realize that you can ask and answer your own questions. And it doesn’t matter if it’s in science or something else.

You have opened a whole world for yourself. So we do that through science because we love it. But really our goal is to create people who are critical thinkers, problem solvers and realize that they can see something in the world that they don’t know about and learn about it.

Let’s take a quick spin through your focus areas. First topic is tools for inquiry. You were just talking about probes and sensors. You really give kids kind of my hands-on introduction to the Internet of Things. Very much so.

Yeah. And really that I think is part of the spirit of what we’re about, which is by no means technology for technology’s sake. Even though we’re all about technology, we’re the first ones to say take a mass in a spring and look at what’s going on.

Take a hand lens, go out in the field. But when there’s something that you can’t easily do with those, then technology really comes to the fore. And technology can inherently be an incredible tool for inquiry, helping students ask questions of the world in ways that give them detailed answers that they couldn’t get with their

own senses. So probes and sensors are one example of that. Yeah. I mean probes and sensors seem innocuous, right? But if it strikes me that some of the most valuable things we can do for young people

are help them ask good questions and then think about the data set behind those questions, how might we question about how could we collect data on something? Who else has data on that? How could we combine those, clean them, interrogate them to answer questions or at least draw inference about questions that we care about?

And introductions to probes and sensors really is probably the first time that young people will begin thinking that way about how they could collect data about something they care about, right? Right. I think it closes the loop between I wonder and wait a minute or I wonder and aha in time

in a way that’s particularly important. We have research to show that feedback loop, if it’s tightened, keeps students engaged and able to recognize that their questions do have answers and that it’s more complicated than that too. What we do is try to almost make science as complex as the real endeavor of science in

some ways as we do try to simplify it so that students are encouraged to mess around with equipment, to mess around with the world and realize that there’s complexity in measuring the world itself, but that they can find answers to those questions quickly and easily sometimes and sometimes they need to take a long time and do it. But STEM models, you have a molecular workbench is a great collection of simulations.

Yes. Why are those important? So they’re a good example of that same philosophy of bringing the world to students through technology in a way that technology is really necessary in that you can do lots of labs, but you can’t do a lab with molecules.

You can’t do inquiry about genetics on your own in 25 minutes in the classroom, but those things are really essential. So a simulation like the workbench takes research grade algorithms about molecular motion and puts a pedagogical layer on the front and makes it accessible for students, well, down to elementary school and even below we work with primary students with the kinds of models

and simulations there to give them a hands-on sense of the phenomena and the ability to explore those phenomena in ways that they couldn’t even begin to reach without technology. I’m a big fan of the FET Sims from CU. How would you say yours are, how are they different? Are they complementary?

I think definitely we’re all in the same zone and when Kathy and the FET group are very close colleagues of ours, I think one of the real distinctions is that because kind of because we’re geeks, we focus on building what we call engines for simulation that can be created and used in multiple purposes. So the molecular workbench is really molecules that can behave according to the laws of nature

and that can be put into lots of different situations so we can create dozens, hundreds of different simulations from any one engine and we have them across the spectrum from molecules to genetics to ecosystems to heat transfer to you name it. Again all with the core science at the bottom so that the results are authentic, the genes are real in our genetic simulations so students can do true inquiry all the way across the

board with them and we can create multiple different scenarios from a 10 minute example to a two week exploration with the same sorts of engines repurposed. And speaking of genetics, you’ve got a, you have a fun engine that explores the heredity and genetics by breeding virtual dragons. Yes, that’s been a staple for many, many years.

A decade before I got to Concord, breeding dragons was part of the whole genetics staple and it’s proved to continue to be engaging for kids and again, we take the same spin. The genes are real, the stories are made up but the genes are real as we say. The horns gene for the dragons is the same gene, literal DNA in our program that gives cattle their horns.

The wings are the ones that give fruit flies their wings so you can actually take those genes and start to use them in biology class in other ways as well. That’s cool. You have a set of modeling tools. Why, why is it important for secondary students to dive into modeling?

It’s really essential. The whole notion of being able to understand not just how the world works, but how to understand and pull out the rules that underlie what’s happening in the world and then use those rules for prediction is really at the core of everything we’re doing, whether it’s science, whether it’s math, whether it’s finance, you name it.

Giving students that ability is key. It’s very complex and very, very difficult to do. I did it when I was a teacher in the classroom and it opened up my eyes to the ways that students could learn in whole new ways. But it’s very complicated to do with topics that aren’t so cut and dried that they’re

almost too oversimplified. So the tools that we have enable students to start with a really low threshold, create almost basic diagrams with arrows and turn those into models that they can run and they can even see data but without necessarily needing the numbers. So it really brings the conceptual sense of modeling in a full loop around for students

in ways that are really important and especially with the next generation science standards. People are recognizing the critical role that modeling plays in science instruction in particular. I’ve been writing about complexity lately and it strikes me that high school, the first affect is boredom. The second affect is simplicity that we’ve turned all the world’s subjects into these

right and wrong answers on worksheets. Right. I guess the question I think a lot about is how do we introduce complexity to young people to help them understand that the world has become very, very complex and it’s the way these complex systems interact with each other and with themselves is it’s grown very

complicated and so how to introduce that topic and I think modeling is a really important way to do that. Yeah, I think you’re exactly right and the interesting thing is that the complexity often comes out of simplicity so it can be quite accessible in that building up a complex system means building up a number of simple rules that tend to have feedback loops and different

kinds of attributes and out of it comes a surprise, an emergent phenomenon, something you didn’t expect and then you’ve got somebody hooked because they didn’t see that coming and in fact the key is that they didn’t see it coming because we don’t see it coming in complex systems. It’s always something emergent and the rules are never complicated, it’s just the effects.

You have a data platform, a common online data analysis platform, what do students use that for? So we’ve been thinking a lot in the last especially five years about the importance of getting students to engage with and learn about data in ways that are exploratory, rich and suit the world that they’re going to be moving into.

I would say today’s fourth grader is tomorrow’s data scientist and when you ask what we’re doing for her, the answer is almost nothing. The code app is an example of a tool that’s building on a rich background of research like all of our work. We build our work on research, we do research on the work that we do, most of our work is

supported by NSF grants. We code app is a way to within the course of 30 seconds take a data set, generate graphs and questions and see connections across the data set because of the way that the tool is created that can open up whole new realms of questions, whole new sets of understanding in ways that other tools simply can’t do.

It does that because it’s built on really the foundation of about 20 years of research and work in two packages Fathom and Tinker plots which were the sort of primary prime stats education tools and are still really well known. People have computers they’re just keeping them alive on because they’re shrink wrap software and code app is the web based successor of those tools.

Every open source available for anybody broadly is becoming much more broadly used now because you simply can’t easily ask and answer questions of data in most other tools. You try to ask and answer questions of data in Excel or Google spreadsheets. In 30 minutes maybe you get a graph but you can’t see connections, you can’t understand the ways that things, one thing leads to another and most importantly you don’t see the structure

and the sort of key aspects of what data is about. So it’s designed for all of those things. You have a cool tool that models the tectonic plates. Yes. What’s that called?

So that’s part of our Geo project that Amy Pellant has been doing a marvelous job with building up a suite of tools that really change the game for the way that people think about learning Earth science. Earth science is this fascinating thing which again, I know we both had a background in thinking about geology but learning geology and Earth science has been something that

is sort of the antithesis of a hands on science. It’s maybe you’re looking at something out in the field but you’re not seeing anything as dynamic or changing and most of the experiments people do tend to lead more to misconceptions than they do anything. So what Amy’s done with these tools like Geo is provide a system-centered view that

is interactive and true to the science again that lets you see the phenomena for what they really are and ask questions and posit your own ideas. So with Geo, the tectonic explorer, you can paint continents onto a globe, you can turn on the forces and watch them smash together and then you can turn it around and you can see that what happens with the other side of the plate when one side is mashing together,

the other side has to be stretching apart because the Earth is a sphere. No other curriculum, no other tools force you to have that realization and help you understand Earth as a system and Amy’s on a mission to help Earth science transform around this idea that it can be a dynamic experiment driven learning process. You have some cool data science tools including a game project.

Yeah, so we’ve been thinking a lot of different ways about how students can engage with the kind of core of what it is to understand the rudiments of data science and we’ve got a set of games for middle school that are more based on statistics and probability and a set that’s more high school based that really is engaging with the ideas underlying data science.

They use Kodap as their engine and engage students with sometimes it’s trying to understand the data behind the BART transport system in the San Francisco but trying to solve a mystery. It might be trying to use data to create a strategy that you can automatically run to win a game against Dr. Markov who’s trying to steal your dog. In both cases it’s requiring you to use data as a way to win the game so you can’t win

the game without understanding the data and then applying your understanding as a strategy in the game. You have a couple of cool engineering design software applications. We’ve been thinking a lot about engineering and what that means for students and how technology can help.

One of the ways that we’ve been doing that is through some marvelous extensive work around solar energy and building design through something called Energy 3D that Charles C. has developed for many years. Within five minutes students can design a house, put windows in, put a solar panel on and orient it in the proper way for their particular latitude and then see what would happen if

they change the latitude. They can build whole cities within this and understand the dynamics of solar design in ways that just aren’t possible otherwise. I would love to dive into math instruction. We’ve talked a lot about active science learning.

You’ve been thinking hard about STEM education now for more than 10 years. How does math fit in? What should we be doing more of and less of? I think there are two frames. I’ve talked a little about data and I can talk more about that and that’s something I think

is critical obviously. Another aspect I think has to do with the experience students have in mathematics class which is most of the time dry, uninspired and not related to the real world or frankly related to what they’re going to do. The answer to that question, when am I going to use this?

Unfortunately, most of the time is you’re not because we’re not teaching you the ways and the things that you will use. Some of the projects that we have working right now in math instruction are deliberately working with research-based curricula that are student-oriented, group-based and problem-based and inherently collaborative.

They are asking the question, what does it really mean to learn as a group of learners? For example, we’re developing a platform where in your learning, as you’re dissolving a problem, in your group, you’re seeing all four screens and of all the other students in your group and you can borrow things from their screen and cut and paste them into yours and develop a group solution and the teacher can watch the whole thing on a true dashboard, not just

a dashboard with some graphs and charts, but to see actually what’s happening so that it becomes a strong tool for the teacher who is facilitating this knowledge-building classroom and orchestrating the conversation that actively that will happen at the end of class as students are participating in sort of constructivist kinds of understanding. So those kinds of tools we think are really critical because there aren’t enough technology tools

that really are built for an inherently collaborative classroom where a teacher is truly a guide and where students are journeying together to uncover the kinds of knowledge that they understand. Mathematics should look like that. Most of the time it doesn’t. Yeah, I did an interview this morning on this topic and it’s interesting to think about the

period in history that we’re in. We’ve spent a couple hundred years teaching math as a solitary sort of theoretical plug and crank of memorize the formula and know when to apply it. Exactly. And almost always done individually. What you just described was one where students are thinking hard about what problem should we

solve. They’re working on it collaboratively. Instead of preparing for a summative assessment at the end of the year, there’s dynamic feedback happening in real time. Exactly. Visible both to the student and to the instructor.

That’s a very different picture of math learning. It really is. And it’s marvelous to see it in action. I’ve been in these classrooms and the teachers and kids are alive with ideas and they in this curriculum, which is a curriculum that’s been around for 30 years.

Actually, it’s a marvelous middle school math curriculum. Students really do learn a different way of thinking about knowledge. By the time they’re through sixth grade and into the sort of seventh grade year that we’ve been piloting in, they don’t think about working, taking somebody else’s results as cheating. They know that they are all building knowledge together.

They know that they won’t know the answer and they know they might not know it for a while. And they know that the teacher might call it Xavier’s process instead of the distributive property because that’s a better way of learning. Eventually, they’ll learn the name for it, but they don’t care. So it’s a really very rich way of learning about things.

And much more true to the way that the workforce is collaborative. You don’t know the answers. We know that we’re just going to figure them out. So in America, we organize math around after we finish arithmetic, we do pre-algebra and then algebra one and then geometry and then algebra two.

And that whole sequence that’s sort of pathway to calculus is really focused on memorizing a set of rules and formulas and knowing when and how to apply them. That strikes me that that construct is really outdated. It doesn’t have much to do with what I did as an engineer or as a finance professional or as an investor.

Why do we keep organizing math that way? And is there a better way to organize a secondary math sequence? That’s a great question. And it’s unfortunate that we probably get so many things in education for so many years. We’ve all seen this happen.

So I think the biggest word in that train that you describe is the word calculus, which has been king throughout. And that started in the Sputnik era. It started when rocket science was rocket science and the thing that we needed to train people for. I took calculus.

I studied physics. I used it. I mean, I did too. And I love Calc and I love Diffie Q. But the large majority of people don’t need,

even professionals who are in highly math intensive situations, don’t use calculus on a regular basis. If you ask how many people use data on a regular basis, the answer is much, much higher. Every day all day. Every day all day, 70, 80 percent.

And the data worker, it’s not just a data scientist. It’s the data worker and that’s almost everybody. So we are preparing students for a world from the 1950s when we’re already in a world of the data age that has come and far upon us. So it’s really critical that we think differently about the way that we learn and apply mathematics.

And I would say that’s both rethinking the math pathways and rethinking where data lives within the curriculum itself. So on the math pathway piece, there are some who are thinking differently about the ways that in Washington state here is one, Oregon is another, there are some other places, but not too many. Thinking about the fact that calculus doesn’t need to be the top and there are other ways to move through this pathway.

And that there are possibilities that are better suited for students, particular proclivities themselves. The students may have interests. Imagine that in what they want to do. And there may be certain math pathways that are better suited for that. That there are ways that students who want to study STEM careers in particular can be guided into calculus, but maybe not everybody.

I think that those are critical to think about. I think it’s critical to think about that in a way that keeps those level so that there’s not the calculus pathway and then the alternate pathways that are lesser. Because I think that there’s a real danger in that. And some people have, there’s really well intentioned work that’s been done in this multiple pathways and where they say there’s a STEM pathway and another pathway. My argument is there’s some value to that, but even the STEM professionals need math modeling.

They need computational thinking. They need less calculation and they need more data science. So let’s not think about preserving the whole model. I agree with you as well, exactly. Which is to say that, that, you know, whether it’s, I think it’s a good debate, whether we even need calculus before college.

I think a lot of colleges will tell you, you probably don’t even. I think it’s not a debate as far as I’m concerned, whether you need to understand math modeling, whether you need to understand sort of math that is learned in a way that helps you understand how to apply. It helps you understand how to work together with other people. That is the antithesis of the worksheets and problems that we get in the back, you know, solving the back of the house. Let’s be clear.

I think we’re not, we’re not attacking algebraic reasoning is the fundamental principle of math modeling. In math modeling, you’re doing multivariable problem solving and you’re doing sensitivity analysis to understand how the variables interact with each other. So. Yes. We’re asking the question, how can you look at that problem with the two trains coming to one another and think about it in a way that helps you see it as a math modeling problem, not as something you’re plugging and chugging.

You need to use algebra because those are the rules of the model that you’re creating, but you need to understand them as the model you’re creating because it’s not just because math modeling is important because that helps you generalize and understand what you’re actually doing. Most of the time when you’re learning to solve that problem, you’re learning, you know, an algorithm or a process that you don’t understand the basis of anyway. So bringing up the math modeling is core in all of that. I completely agree. So if we step back and think about what high school could look like.

If you want to start a couple of grades earlier and talk about secondary school. What would some education look like described as sort of experiences and sequences that young people should benefit from? Right. I think it’s the experiences that are most important. The sequences, there are certain things that need to come before others.

There are things that we know about learning progressions. But most important is a student’s experience of science learning, of mathematics learning should be one of where they recognize that they are active agents in the discovery of knowledge from an early enough age that they realize that that’s the way school works and that they haven’t. It hasn’t been beaten out of them or learned out of them from us at the time they get to high school. It’s complicated to even get them to think as sort of independent learners. Learning like that looks like project based learning, problem based learning most of the time.

It looks like applying these kinds of ideas we’ve been talking about as active tools. So it looks like students are not really interested in what they’re doing. So it looks like students recognizing there’s a problem to be solved, knowing there is a tool to be reached for and reaching and being fluent enough to reach for that tool. Whether it’s a data tool, a modeling tool, a tool of sort of understanding and discourse, a tool of collaboration. Some of those might be technology tools.

Some of those might be protocols or just understandings of how they work together. Fluent in the ability to recognize that knowledge is uncovered, that we work together to uncover it and that if there’s a problem there, there’s a solution to be had and I can find it. And that I think is the thing that is often most lost in education right now is that we’re waiting for that. It’s when I know that you know the answer, then we’re both waiting for that. The magic time in learning happens very, very rarely, but it does sometimes.

We’ve all experienced it, say in the science lab or something. When a teacher comes over and you say, what about this? And you see the brow furrow and they lean in and you realize in that magic moment that neither of you know the answer. And all of a sudden you realize that your idea is as valid as that teacher’s idea. And finding a way to make that happen all the time is really what’s key.

That it’s not, I’m just trying to find the answer that’s in your head. It’s we’re working together because the answers are out there to be found. That’s the key. That’s a change for teachers to be able to walk into that moment and say, I don’t know. That’s a new sense of vulnerability to bring to the classroom.

Yes, it definitely is. And in professional development work that I’ve done, I’ve seen how complicated that can be. And I’ve also seen both in professional development work and in my own work as a teacher with colleagues, it’s probably the most purely invigorating thing a teacher can experience as well. When a teacher realizes that the process he or she has been going through for maybe years or decades

has been creating students who don’t actually know the things that they are parroting back and realizes that there is a way to get them to that. I’ve seen teachers who are going to retire continue on for 10 more years because they said, ah, I’ve never known it could be like this. Let’s wrap up with some concluding thoughts on how to help teachers

be successful in this new environment, in this new approach. So what are the on-ramps to help them begin to lead these sorts of classrooms? That’s a great question. That’s one that we’ve been asking ourselves as well. I think the answer is in finding ways for students to have agency,

whether they be small or medium or large, and usually they’re small at the beginning. And realizing that that agency is based in content, that students can ask and answer their own questions about the kind of content that they’re learning and that you can take the labs that you might have been doing in science class and turn them into inquiry-centered labs that evoke the practices, sometimes just by flipping them on their head.

And that it’s okay and that it’s okay not to know the answer. It’s okay for students to be confused and it’s okay to let them sit in that for a while. Sometimes it’s okay for things to be messy. Data can be messy. The beginning of a lab doesn’t necessarily need to be the setup instructions.

It might be figure out how we might measure this and that providing a little bit more of that leash in structured ways is ultimately a very satisfying on-ramp for teachers. We have found that for really big changes in instruction that school visits or classroom visits can be really powerful. It’s immersive learning for teachers. Are there things like that? Do you recommend school visits or classroom visits or can videos be helpful?

Yes, I think school visits and classroom visits are very powerful and I’ve seen that back in the days when I was a teacher and looking at school reform and changing minds about the way that school could be. I think videos can be a good proxy for that. It’s hard to make good videos of that but they can be very powerful when they are. I also think that student work can be extremely powerful especially student work that uncovers misconceptions students may have long held

that haven’t necessarily been addressed in the way that a teacher might have thought. When I was working in the May Math and Science Alliance some of the most powerful things I saw were individual question probes that you could give to teachers that deliberately were designed to tease out students misconceptions and we just give them to people ahead of time and say give this to your students and bring the work into the session and they would come in already armed with

wait a minute we’ve got to talk about this because they looked at the results and said we just taught that last week and the discussions that happened among those teachers were more engaged or richer were more life-changing than I’ve seen in almost any other opportunities because teachers realized that there was something different that they could do to get students to learn differently and it came from their actual students and their actual work and that conversations around student

work are always powerful. Are you optimistic? Do you see uptake of the the tools that Conker is sharing? Yes it’s it’s very exciting to be in a time when this work can come to fruition. I’ve been saying a lot lately that in some ways I’ve been realizing recently all the work that we’ve been doing for 25 years was really essentially theoretical for the majority almost all that time not as if it was not being used we had teachers using it but it could never really be used at scale

because the technology wasn’t there the realizations weren’t there people’s awareness wasn’t there that’s all really changed in the last three or four years and now this is truly possible and now we’re starting to say okay let’s let’s talk about this stuff we’ve been doing for the last 25 years because it’s all in the same vein and now we can see it happening you know at large in schools around the nation in countries around the world in ways that are really starting to take take root

and because at the heart of it this essence of the world itself those phenomena don’t change they’re still there and the idea of putting students in the driver’s seat is all sort of at the core when it propagates it propagates in ways that are rich and learner-centered and very powerful all the time and that’s really heartening. It feels like it’s kind of a perfect storm for Concord schools are adopting broader aims your tools and other tools are getting better and

more accessible there’s a lot of movement towards student-centered learning more project-based learning the shift to formative that that we talked about all of that feels like it’s creating a big opening for what you’ve been focusing on for 20 years. It really is yeah one of our advisors in the world’s coming in a Concord direction and I think that’s really true and and we’re seeing teachers I think more open to this than they ever have been especially with the next generation

science standards the the way that that has come about has really helped people see their work in a different light and for the first time I think ever for me in education reform I am seeing teachers not pushing back but rolling up their sleeves and saying yeah that makes sense you know help me how do I do it and that’s just incredibly exciting yeah. Chad Dorsey thanks for joining us on the Getting Smart podcast. Thanks so much it’s a pleasure to

be here. A big thanks to Chad for joining us on today’s episode we appreciate the Concord consortium’s work to bring STEM education alive for more students. For more on intelligent uses of tech and math education check out episode 230 with Jesse Woolly Wilson of Dreambox Learning. We’ve got it linked in the show notes and on the blog and before you go be sure to rate and review the show so more people can find us. That’s it for today listeners thanks for tuning in for

the Getting Smart podcast this is Jessica signing off. you