Can AI Make Math Class More Social? | Britte Haugan Cheng & Ann Edwards

In this episode of the Getting Smart Podcast, Nate McClennen talks with Britte Haugan Cheng and Ann Edwards of the AIMS Collaboratory, a Gates Foundation-funded network working to improve math education for students furthest from opportunity. They talk about how developers, researchers, teachers, and students build edtech together, what pro-social AI could look like in the math classroom, and what leaders can learn from a network that shares what it learns.

Transcript

Nate McClennen: Welcome, everybody, to the Getting Smart podcast. Today we’re talking about three really interesting areas for us at Getting Smart, and I think for education in general. In combination, we’re gonna talk about math education, we’re gonna talk about education technology, and we’re gonna talk about networks.

This is of interest to us, and I think to you as listeners, because in the math world, as we know, we’re still challenged by proficiency for all learners. We know that students are disengaged from math, and we know that not all students meet growth and/or proficiency for grade level. So we need to figure out solutions to that, and our two guests today are really thinking deeply about this.

Second is edtech. We recognize that there is a lot of edtech emerging right now, especially with the advent of AI, and much of it may not be produced or tested in partnership with those who are going to use it, in other words, practitioners, especially in early stages.

And finally, especially at Getting Smart, we’re really interested in networks, and how we close the gap between people who know things and people who wanna know things. How do we share more liberally? How do we reduce friction? And especially in R&D networks, of which the AIMS Collaboratory is part. We’re really good at implementation in the education sector, the science of implementation. We’re not as good at true R&D, where we’re actually thinking about research and development. Unlike the corporate world, which really digs deep into the R&D sector, education does not do this as much, and I’m super excited to learn a bit more about that today.

So today we’re joined by Britte Haugan Cheng and Ann Edwards from the AIMS Collaboratory, which is a Gates Foundation-funded network dedicated to advancing educational research and practice in mathematics. Britte brings extensive expertise in research and assessment, and Ann specializes in mathematics education. As a former math teacher, I taught high school math for many years, so I’m excited about this conversation. Welcome, Britte. Welcome, Ann. Excited to have you here today.

Britte Haugan Cheng: Thank you. It’s great to be here.

Ann Edwards: Thank you so much, Nate, for inviting us.

Memorable Math Classes: A Teacher Strike and a Geometry Puzzle

Nate McClennen: Absolutely. And our first question, the one I always ask our podcast guests: think back to your K-12 experience. What was your most memorable math class? And by memorable, I mean you were engaged, you felt like you belonged, and you actually learned something in that math class. So Ann, why don’t you start us off, and then we’ll go to Britte.

Ann Edwards: So Nate, thanks for the invitation to reflect on my own mathematical experience. I’m actually gonna take your question in a slightly different direction than you might expect. I’m gonna talk about an opportunity I had my senior year in high school, when our teachers actually went on strike.

I was an aide to an AP Calculus class, and I had taken it earlier and done well, so she asked me to help, and actually, specifically, she asked me to teach it while she was on strike. She called me every night and told me what to cover, and it was crazy, and one of the most impactful experiences of my life, because I thought I knew it, and I clearly didn’t.

I really came to grapple with this idea of what it means to know mathematics. What does it mean to know mathematics to teach it well? What is mathematics teaching? It’s not just telling people a bunch of stuff, because telling people a bunch of stuff doesn’t mean they learn it. So it really was a super important experience in my high school mathematics that has shaped how I think about mathematics, learning, and teaching ever since.

Nate McClennen: Ann, I wanna go a little bit deeper into this. So you were given the power of being in front of a group of your peers. I assume they were 10th, 11th, and 12th graders, the teacher was not in the room because the teacher was on strike, and you had to teach the class. Is that the picture I’m trying to paint here?

Ann Edwards: She was not in the class. There was a teacher of record who read a newspaper and, you know, had a whistle. So what you describe is, yes, what happened.

Nate McClennen: Amazing. Well, that is a unique experience, and I’m sure you learned a lot. Britte, what about you? K-12 math.

Britte Haugan Cheng: I remember high school geometry really vividly, because I started off that class feeling really competent and confident, and like, as you say, Nate, I belonged in that classroom, which I hadn’t, very much, at that point. I was very much a science kid, but not a math kid. So I was really excited about geometry being a puzzle I could solve, and I knew how to find the solutions if I didn’t immediately understand how to solve a problem.

But midway through the year, I started to struggle. It was in that same class that I had the experience a lot of people have of feeling like they’ve run up against their limits with mathematics. It’s that same class where I felt really excited, and then also started to really question whether math, higher math, was gonna be for me.

Nate McClennen: Yeah. I love that idea of struggle. It’s hard, but so important, and how do we get through those barriers? I’ll have to share really quickly: I had an analysis class as, I think, an 11th grader, and I remember very specifically the teacher, Dr. Garabedian. We were in chalkboards and chalk at this point, and he was a lecture-based teacher, which I’m not sure would necessarily… it worked well for me. But he made it very clear that we belonged in that class, and he’d come in and dust his hands off and start writing proofs on the board, and we’d all just eagerly dive in, and he knew every one of us and what we liked, et cetera. So I just appreciate that idea of belonging, even if the content was really hard.

What Is the AIMS Collaboratory?

Nate McClennen: All right, let’s jump to the present. Thank you for a little walk down the past. The AIMS Collaboratory. Two things I’m interested in. What is it, for our listeners who may not know anything about this really impressive project? And the second part of that question is, what does the R&D cycle look like at AIMS, and why is it really, truly R&D? You all can decide who’s gonna answer what, but I’m curious about both of those questions.

Britte Haugan Cheng: So, Ann, maybe I’ll start and then you can jump in, especially with your experience of seeing the R&D cycle with teams, because Ann and I get different views into the teams’ work. The AIMS Collaboratory is, as you say, an R&D network, and it is a group of teams who are working on educational technology to support students who are furthest from opportunity. You might hear us use the phrase “priority students,” because that comes from the foundation language, and that is Black students, brown students, and students experiencing poverty. So students who have historically, and currently, been furthest from opportunity, that’s who we’re trying to serve through the network.

A lot of the teams are already made up of researchers, developers, and practitioners. And when we say “practitioners” in this conversation, we mean both classroom teachers and also district administrators and coaches. We get a range of practitioners coming into the network. These teams are working together to learn from one another throughout their own R&D journeys, which are already richer than a typical R&D partnership you can think of. They collaborate through mini grants that we offer through the network, and they’re all producing what we call public goods, which are tools and other resources that are available to anyone outside of the AIMS Collaboratory to use as part of their own research and development. So we’re really proud of the work the teams are doing. And Ann, I don’t know if you wanna take over from here about the R&D cycle?

Ann Edwards: Sure. What we try to build capacity in these teams to engage in is what we think of as deeply inclusive R&D. That means from the get-go, practitioners, researchers, and developers sit alongside one another to investigate the problem they’re trying to solve, what the root causes of that problem might be, how they can design to address those causes, and then how they can continue to refine and learn as part of a research agenda together. Walking down that road together allows for solutions that are at once meaningful and useful to practitioners, that also reflect the design and interest goals of the developers, and that draw on researcher expertise. That is a design that sets us up, as a field, to create solutions that better meet the challenge, Nate, that you raised for us at the start, that AIMS is trying to address, of really improving the mathematical learning experiences for all students.

Nate McClennen: Got it. And just to go a little deeper, Ann: do you use a specific structure for R&D, or is it looser, where any team can design what works for them? How are you actually structuring the R&D process?

Ann Edwards: So each of our teams is organized differently. They work on different products. They have different goals. As such, the structure of how R&D works is gonna look a little different. There are principles that drive this work across teams, of course. Co-design is a core pillar. Co-design means that, as I mentioned, from the get-go, in all aspects of the research agenda and design process, all the voices are included. But we draw upon lots of different models of what that could look like. Britte and I help lead what we call the facilitation team of AIMS, and the facilitation team is made up of folks who have prior expertise and experience in lots of different ways of doing collaborative R&D, engaged learning networks, and so on and so forth. So we bring different perspectives and support these teams in their particular configurations with their particular goals.

Nate McClennen: Got it, got it. Makes sense. So flexible co-design is a key ingredient there, flexible based on the context and what problem they’re trying to solve.

Public Goods: Sharing What Teams Learn

Nate McClennen: Britte, back to you. I’m thinking a little bit about public goods. We assume, especially in the public sector, that a lot of things are public goods, but the ability to share them, when someone creates something in a classroom or in a school, is very limited. So can you give some examples of what that looks like, or how it works in this concept of public goods?

Britte Haugan Cheng: Yep. So there is a challenge, as you say, of folks being able to share out their learnings, especially from the practice side of the house. But there are also developers who are very much in the business of making sure their technology is working and doing well, and that they have a role in the market. So the fact that we have teams who are coming together and are willing to put skin in the game, literally, by sharing what they’re learning publicly, is quite a feat. I think that’s part of the structure that comes from the foundation, but also the commitment of the teams who are part of the Collaboratory.

What it means is that teams are developing tools as they go through the research, and they may be sharing those out, including measures or methodological approaches, what they’re learning about practices. They might be sharing guides or practice briefs. But then they’re also sharing, potentially, data sets that they’re generating that might be helpful for others to use as part of their research. There is actually a sister program, AIMS EduData, that provides grant opportunities for researchers to come and use the data sets that have been put together by the AIMS teams and their platforms to conduct further research. So it’s making available many of the very expensive steps of the R&D process to other researchers, to come and get involved.

And then, of course, there’s the dissemination of the findings. That’s a more typical process that we think of with R&D. But we see teams doing a lot of during-research sharing of ideas at conferences. So you’re not waiting three, four, five years for a study to come out in a peer-reviewed journal, which of course we want to see, but we also want to know what people are learning along the way, understanding that that evidence comes with some caveats, because it hasn’t been through all of the peer review, et cetera. But those are really important findings as people are going through R&D in this very accelerated R&D world that we’re in.

Nate McClennen: Yeah. So I assume, if I could imagine this, there’s an edtech product. A company decides they’re building something. They have an idea that is based on market research and on needs and challenges to be solved, and they’re bringing early-stage versions of this product to these teams to help co-design. But in the process, the public good side of it is actually opening up their doors a little bit and sharing what we’re learning, right? Is that the gist of what you’re saying?

Britte Haugan Cheng: Yeah. And for market competitors, that’s quite a thing. So we really have to take our hats off to our partners.

Implementation, Variation, and the Value of Networks

Britte Haugan Cheng: And I wanna bring implementation back in here, Nate, if you don’t mind, because I actually wanted to challenge the statement you made at the beginning of the conversation a little bit, that education does really well with implementation. I’ve spent a lot of time thinking about how we’re not doing that so well in our space. And in fact, this is one of the things that’s most important when we think about addressing equity and supporting students furthest from opportunity. We really have to think about what works for whom and under what conditions.

I’m sure we’ve all heard that phrase, but it’s really true, and I think it’s becoming even more pressing in our field that you have a classroom of students who are quite diverse from one another, linguistically, culturally, maybe even in terms of their conceptual achievement in mathematics, but also just in their attitudes and identities around mathematics. We’ve really got to think about how to support students holistically across all of those dimensions, to really address what it means to support students equitably across the board. So we really think about implementation R&D as a really important track. It’s one thing to develop a tool really well, or to develop curriculum materials or teaching practices, but it’s another to think about how to implement them well when you’re in a classroom that’s quite diverse, or even not.

And implementation’s tough, and I think we, as a field, haven’t gotten really good at talking about how the different variables of context impinge on implementation, and how we support teachers in doing that well. Some of the teams in AIMS are really taking on implementation as a thing they’re trying to understand better, and I, for one, couldn’t be happier about that.

Nate McClennen: Yeah, no, I appreciate the pushback. And I think that’s something we’re thinking about as well. Implementation science, meaning the literature in it, is well developed. The implementation of implementation, if I’m gonna use it in a complicated sentence, is… I really appreciate the conditions and the context, and how we’re not as strong at that, and how that differs from, you used the word implementation R&D, and I think there’s this mashup of terms that we use, of innovation and continuous improvement and implementation science. So I appreciate the clarification.

Britte Haugan Cheng: Yeah. But this is one of the real benefits of a network, right? We can actually start to talk to each other about those conditions and about the variations that we’re seeing. The variation is actually key and wonderful. It’s where the really good, rich R&D is. I think we think of causal RCTs as eliminating variation in our R&D. But what if we actually wanted to understand that variation better, so that we could better support students across the board? The network really provides an opportunity for us to do that work in earnest, and I think that’s a real innovation in the field.

AI in the Math Classroom: Hype or Pro-Social Tool?

Nate McClennen: I’m gonna come back to the network, and I’m also gonna have you define causal RCT for everybody, because I’m sure many of our listeners don’t know what it is. But Ann, I wanna jump quickly to math education, because I’m thinking about technology specifically. I’m thinking about artificial intelligence, and we’ve had a lot of conversations on this podcast about AI and how it’s gonna impact education and the world writ large. How are you seeing, in the network, this idea of hype? I think many edtech companies are scrambling to say, “Ah, we’ve gotta put AI engines in, and we’ve gotta have generative technologies in our systems.” How is that playing out in this network, which existed prior to AI becoming more mainstream? What does that look like, Ann, on your side?

Ann Edwards: It’s looking in a couple of different ways. One is, by virtue of the way we do inclusive R&D in the network, developers are getting a firsthand view on what actually matters in mathematics teaching and learning on the ground. One of the emerging ideas that is really taking hold, that I’m super excited about as somebody who thinks about mathematics teaching and learning a lot, is what we talk about in terms of pro-social uses of technologies, and in particular AI.

The reason I’m so excited about it is that we have long known that mathematics teaching and learning depends on rich conversation, problem-solving, reasoning, and justification that’s made visible in the classroom in ways that allow teachers to be responsive to it. So we want to be able to create technologies that support the advancement of that kind of rich discourse in mathematics classrooms, and that is what we’re beginning to see now.

That is one of the things in the network that I would not call hype, but more of an emerging trend based on real need that’s grounded in evidence-based mathematics instruction and what teachers are saying they really want and need. We have lots of examples of pro-social uses of technology in the network, but all of them are seeking to harness AI to inform teachers, and in some cases even students, in ways that promote them to share their thinking, and promote teachers to ask good questions that are grounded in what students are thinking and saying and doing in the moment. That is a capacity that really great teachers have had for a really long time, and it’s really, really difficult to do. Harnessing AI, especially, to be able to do this effectively and efficiently, I think, is super promising. That’s not hype, it’s really important, and I think it’s a potential game changer in this space.

Nate McClennen: Yeah, no, I appreciate that: the pro-social use, and AI as a partner to advance the things we know are really, really effective in the math classroom, rich conversation, discourse, and visible problem-solving. I remember having students go up to the board and solve problems, and then argue about them. They would put the same problem on the board and say, “Wait, my solution versus someone else’s solution.”

Ann Edwards: That discourse… I actually wasn’t thinking much about learning and discourse at that point in my career, but that’s what I was doing, right? Exactly. Now there are tools that can actually look at what students produce, hear what students are saying in the moment, grok all of that together, and then give suggestions both to students, “Hey, have you thought about this? You might wanna consider this idea,” or to teachers, right? “Why don’t you ask students about that? I noticed that student A and student B had similar, connected solutions. Why don’t you ask them to explain that?” So there are ways these technologies can do far more than an individualized chatbot that just tells folks things. There really are technologies that advance exactly what we know is important to do in mathematics classrooms.

What Is a Causal RCT, and Why Context Matters

Nate McClennen: Love that. Let’s go back to RCTs, just to get a quick research primer. When you say causal RCTs, randomized controlled trials, I understand the variation point. Can you elaborate a little, to help our listeners understand why that statement matters, Britte?

Britte Haugan Cheng: Well, I’ll say right away you’re asking probably the wrong person, because I tend to believe that RCTs have their limitations, like all methodologies.

Nate McClennen: Fine. Good answer.

Britte Haugan Cheng: But people think of RCTs as the gold standard in research, because it is a design that eliminates a lot of variation. What you’re trying to do with an RCT is narrow down the particular intervention you’re studying and say that it is the intervention causing the impact we see. So you do a lot of methodological fun magic to make sure you can say, with the best knowledge you have, that the impact you might be seeing in a study is directly due to the intervention you’re studying. That’s why people call it causal. We’re saying this causes that, as opposed to this is correlated with that, which is what a lot of research has to say, because there are a lot of things that interact with implementation. There are a lot of things that interact in the system, so when we see an outcome, we know it’s probably related to the intervention we’re studying, but we can’t always tell to what degree. So there are different methodologies for studying the impact of an intervention, and people like RCTs because they think it gets as close as possible to saying that the intervention is responsible for the outcome.

I have a lot of criticisms myself of using that as the guidepost for our field, for many, many reasons. For one, you usually need to make sure you’re studying something you understand very well, which means it’s a very mature intervention in terms of R&D. You’ve been understanding its implementation variables for a long time. You know what to control for, you know what to look for. Running RCTs on less mature or less well-understood interventions means there’s a lot you can’t interpret in the system. So there are a lot of ways we like to use all the tools in the trade to understand what we’re studying.

Nate McClennen: Right, right. And I can imagine it goes back to context, right? You might have an intervention that you actually show works in a classroom, an edtech tool in a teacher’s partnership with a particular set of students, but in an RCT, it may not show that.

Britte Haugan Cheng: But in that classroom, it actually did work. That’s right.

Nate McClennen: That can happen, right? It can happen contextually.

Britte Haugan Cheng: And I think principals and superintendents face, a lot of times, very early evidence of whether something is working or not, and have to make decisions about that a lot. We think about that often. We know there’s a thing called an implementation dip: when you start using an intervention for the first time, teachers, or whoever is responsible for the intervention, need time to get to know it and understand how best to use it. So sometimes you see a dip in the outcomes you care about, and a lot of times that gets confused with something not being effective. So we’ve gotta think long and hard about the evidence, especially around gen AI and tools in this space, about what the early evidence is telling us, how we should frame that evidence in terms of our decision-making, and really understand what it does and doesn’t tell us about what is working and what is not, and for whom.

Including Students in R&D

Nate McClennen: Right, right. Okay. Thank you for the clarification. Super helpful, Britte. Let’s spin back to students. I know students are also involved in this process. They’re obviously the recipients, or participants, in these R&D cycles. What’s their role? How are students included in the process, in feedback, et cetera?

Ann Edwards: I can take a stab at that. So we do that in AIMS in a couple of different ways. One is, across the network, we convene something we call the Youth Advisory Panel. This is a really incredible group of young people whom we’ve worked with in AIMS over multiple years. They inform how AIMS operates, what themes are brought to the table, how we engage the teams, and what the teams themselves really need to be paying attention to, from the vantage point of youth who are learning mathematics, who are in schools and institutions, who are grappling with the uses of technology in multiple ways.

Teams themselves also use different forms of things like youth advisory panels, or young people in different ways. They might organize design sprints that can run over days, weeks, or even terms, to help inform and engage with them in the ongoing design process. We also engage with different kinds of partners who have the capacity and a mission to elevate youth voice and youth engagement in various education enterprises, and to empower youth in doing that. BUILD is one that we’ve worked with really closely over the years. They are a youth empowerment organization, and their engagement with us and with the teams has been incredibly valuable.

While individual developers or individual schools and districts might not have the capacity of something like the full AIMS Collaboratory to engage in this work, I think we are learning a lot about how to engage students and elevate student voice, in work that schools and districts, or developers, might be able to take into their ongoing work. So I’ll leave it at that. Britte, I don’t know if you wanna say more about that?

Britte Haugan Cheng: Thank you for that list. It makes me happy to hear and remember all the ways in which we’re elevating youth as part of AIMS. It’s such a cool thing we get to do. We do twice-annual convenings. We travel to a partner district location, and we often bring teachers and students from that district to the meetings. So we’re trying to connect students up with all of the teams in a lot of different ways. We try to make sure that teachers and students are engaged across the board. One thing I do wanna say is I think we’re learning a lot, like you were saying, Ann, about how to elevate both teacher and student voice…

Nate McClennen: …and how those feedback cycles can actually be part of R&D, but specifically development.

Britte Haugan Cheng: When we think about developers, they’re on a timeline, and it is not the traditional academic research timeline, right? So you get a lot of friction between those timelines and timescales. Making sure teacher and student feedback are part of that is really, really important, but difficult to do. We’re seeing a variety of ways that teams are doing that, but not everybody has the opportunity to do it as much as others. But I’ll name one team, Stimuli, that built a league of student co-designers that they meet with, I think, weekly, I think every Saturday. It’s a real example of deep commitment to the students they’re hoping to serve, through elevating their voice in the design process.

Joining the Network and What Networks Teach

Nate McClennen: Right. Awesome. Yeah, really appreciate the role of students in this work. We’re thinking about it writ large: what does transformation look like in the education sector, where does student voice come in, where is it ignored, and how do we elevate and amplify student voice? Let’s pivot, in our last section here, to the network itself. As I said at the beginning, that was the third piece I was interested in, this concept of the network. I think I have two questions. One is, how do you join the network? Can anyone join? Is it a proposal process? Those kinds of things. And the second is, what have you learned about this Collaboratory over the years? What’s the meta, if you had to look at it from a balcony view, about the functioning of networks? Because there are hundreds and hundreds of networks in the ed sector. What have you learned? Two questions: how does it work, and then what’s the balcony view?

Britte Haugan Cheng: I’ll say that the AIMS Collaboratory started off as a closed network of Gates Foundation grantees, and that has slowly been opening. We’re starting to build an external layer of our network where folks can keep in touch with us. And of course, the best way to get involved, if you’re a practitioner, is to start working with some of the developers who are already in the network. That is a very good way to do that. For researchers, I mentioned AIMS EduData, a grant program for researchers to get involved. So it is still primarily a closed network, but we’re working on making sure that not only are the public goods open, but there are ways to engage with our members more directly.

We’re working on that. And I think that is a learning, to your point about networks: how do you build a community like this? How do you build a community where people have the trust and the long-standing relationships to do this difficult work? And then how can you open that up? The answer is that it has to be done very carefully. We’re very protective of the community, because they are putting a lot on the line, in a lot of ways, to collaborate with one another. But we’ve also learned that you have to support them in very specific ways. You’ve gotta give them the resources to collaborate, which is not just time, but in some ways additional funding to do that. As I mentioned, the Collaboratory has a mini grant opportunity for teams to collaborate together. So we see teams blending their superpowers now to come up with even more amazing ways to support teachers and students.

We’re also learning that the ways you scaffold the conversations between the teams really make a difference. The facilitation team, where Ann and I have been working on ideas about how to create collective action in R&D for quite a while, thinks long and hard about how to facilitate that kind of conversation. So that ingredient, not to toot our own horn, is actually quite critical. And we have to have courageous leadership from the foundation to give people the space to do this. I’m sure there are many other lessons I’m missing here. Ann, please take over.

Ann Edwards: I think it’s a combination of having really strong norms and values that center all the work, and Britte named a couple: trust, respect, and a shared commitment and mission that we hold for advancing solutions in mathematics for our students of the highest priority. And we have to have structures and processes that support and enliven those commitments, right? We do a number of different things, for example, to better understand, listen to, and lean into what the teams are doing and what they need, their challenges and interests. And then, on a regular, systematic basis, we bring that to a number of different opportunities, resources, and engagements they can participate in.

I would also say we are always thinking about ways to connect the teams to one another, and Britte named a couple of those mechanisms. But it is very much a constant work in progress of keeping our ear to the ground in terms of what folks are saying, and then designing for that. And the last thing I would say is we are engaged in our own learning project about how to do this networked work. So we operate an additional reflective layer that carves out time and space for us to think about what we’re learning, and then share what we’re learning about how to do this kind of thing. In that way, we hope to contribute to the ongoing conversation about network improvement, or knowledge networks, or deep R&D in collaborative spaces across teams. So there are lots of different mechanisms and structures at play that I think really drive the work and what we’re learning about it.

Britte Haugan Cheng: To add one last thing, to answer another part of your question, Nate: we do bring folks to our convenings from other networks, into the space, to have conversations with us. I think we all know that funding for R&D is getting harder from all sources, but especially federal sources. So the role of networks is becoming, to me, ever more critical in ensuring that we’re doing the work that needs to be done to support students, especially when funding for actual teaching and learning is also on the decline. I think the role of networks in that funding ecosystem becomes ever more important, and so is sharing out what we’re learning.

And by the way, you can read some of our learnings about how to run the network in our inventory of public goods, which is available on the AIMS Collaboratory website. That’s a very easy way to find what we’re learning, from the individual teams up through our learnings at the facilitation level. But I think this notion of ensuring that the networks being built are thought of as proper infrastructure in and of themselves for R&D, and that they’re also in connection with one another, is more important than ever. We’re really excited for anyone else who’s doing network work to reach out to us to talk with us and to co-learn with us. Ann and I do that as part of our day-to-day job all the time. We’re talking with folks who are running other networks, so we’re open to that kind of collaboration all the time.

Ann Edwards: I’ll add one other thing. We, with great intention, are also seeking to share learnings with our practitioner stakeholders. We work very closely with folks at NCTM, who really represent the math teachers in the country, to share learnings and involve their constituencies in engaging with us in a number of different ways. We think that’s a really important part of our mission, to share learnings with the broader population.

Nate McClennen: We help support a couple of networks in more general school transformation, in Virginia and Kansas City and Michigan, and Nevada is emerging. We’re also thinking about how networks themselves function, this meta look. And I really like that you’ve carved out time for that, so I will certainly jump onto the public goods section of the website to take a look at your learning. That’s exciting.

Advice for School Leaders

Nate McClennen: Let’s wrap with a question. If there’s a school leader listening to this, and they either don’t have the bandwidth or aren’t connected to the AIMS Collaboratory, but they’re struggling with math in some form or another in their system, whether they’re a superintendent, principal, coordinator, et cetera: in the simplest way possible, what’s the most concise advice you could give them? Maybe Ann, you start off, and then Britte, you close us off.

Ann Edwards: Do you mean in the context of this work with edtech?

Nate McClennen: Yeah. Anything. Just what’s on the top of your head. Like you bump into someone in a coffee shop and they say, “Wow, I’m a principal of a school and our math scores are struggling,” or “Our math classes are not as engaging,” et cetera. What’s the advice you give them?

Ann Edwards: Yep. I think one is, look at your data, to better understand, under the hood, what your strengths are. Is that instructional strengths? Is that leadership strengths? What are some of your areas of growth? Is that coaching, or your instructional materials? And broaden your notion of what data counts. It’s not just outcome data, it’s also data from the people actually on the ground and their experience.

That will then inform the next set of questions you’ve gotta ask yourself: what resources do I need to provide? What capacities do I need to build? In an era where states and districts are now thinking a lot about high-quality instructional materials, what do I think, as a leader, about high-quality instructional materials? What do I need to be bringing to my folks to ensure that they have the best for them in our context, that they can work with and draw upon? And then, how can I best support the best implementation? These are all important and sometimes very expensive questions, I understand that. But I think part of the quandary leaders face is an overabundance of information, of data, of choices. If you can really center that journey through understanding what you should do, by looking at where you are and what you need, and centering your instructional vision, that’s the way to go.

Nate McClennen: Yeah, I love it. There is certainly an overabundance of information, and it is hard to make those choices. So all right. Advice number one. Britte then walks into the coffee shop, same person’s there. Britte, what do you add to this? What’s your recommendation?

Britte Haugan Cheng: The first thing I always say is listen to Ann Edwards. Do all the things that she says, and you can’t go wrong. And I would just elevate two pieces that I wanna make sure the listeners hear. One is that the expertise from practice is a deep knowledge base. Even though there may be a lot of information coming from R&D, and a lot of materials coming from developers, what we know about good teaching and learning is still what we know about good teaching and learning, not the other way around. So, as Ann says, keeping your instructional vision at the center is a good way to go.

And then the last thing I’ll say is that we’ve learned, and there’s a lot of research that backs this up from other sectors as well, that designing for priority students, designing for students who are historically and currently marginalized, benefits all. So we have an ability to simultaneously serve the students who are most in need and know that we’re gonna be doing good for all of the students in any particular system, and we shouldn’t be afraid to embrace that. Our design results speak to that, and so I hope people feel like they can stick to their vision and support those students who are really struggling.

Closing Takeaways

Nate McClennen: Appreciate it. Really good advice for our coffee shop talk. So I walk in and chime in: “Wow, I listened to this great podcast with Ann and Britte from the AIMS Collaboratory, and here’s what I learned from that podcast.” You ended with this idea that designing for priority students benefits all, and I think that’s a really important takeaway. And the idea of keeping the instructional vision front and center, based on learning sciences and what we know, the deep knowledge base about that, not the other way around.

A couple of other things that were really important to me, at least, in this conversation: this idea of public goods, that even in the nonprofit and public ed sector, where we’re all doing our own thing, sometimes there are closed doors, right? So how do we liberally share with others? And I appreciate the AIMS Collaboratory doing that. Inclusive R&D, co-design, using students and teachers as primary contributors and active engagers in the R&D process. We took a little bit of a step into what matters in math education and this idea of pro-social AI and technology use that engages with rich conversation and discourse in the math classroom and visible problem-solving. Really important. The fact that there are gonna be implementation dips, and that’s well documented: you start something and it may not work. And how does that reflect politically to the school leaders who have to defend this against a school board or parents, et cetera?

And then finally, the last thing that’s really intriguing to me is what you’ve learned about building community: that obviously trust and openness matter, and you have to protect that. Resources matter, money and time. Scaffolding conversations between teams, and how one team interacts with another team, is really important. Obviously, norms and values. Listening, listening. Keep your ear to the ground, which is probably good in the world writ large right now. Be curious and listen a lot, right? And then thinking about the meta of it: how are we reflecting on the work we’re doing with networks?

Ann, Britte, really great to talk to you. I learned a ton today. Hopefully our listeners did as well. And just keep on doing this great work. It’s really amazing and helpful for math education in the United States, and for all these teams that are working hard to focus on those priority students who are really willing and able to contribute when given the right environments and contexts. So thank you so much.

Links

• AIMS Collaboratory: https://www.aimscollaboratory.org/
• Inventory of Public Goods: https://www.aimscollaboratory.org/resources
• AIMS EduData: https://digitalpromise.org/initiative/advancing-innovative-math-solutions-aims-home/
• BUILD: https://build.org/about/
• NCTM: https://www.nctm.org/