157 – Innovative MSc Programme for Maths Teachers in Kenya, Part 2

[00:00:07] David: Hi, and welcome to the IDEMS podcast. My name is David Stern, I’m a founding director of IDEMS, and it is my privilege yet again to be here with Mike Obiero to continue our discussions from a recent episode about your MSc program, this MSc in Math Innovation. It’s a really exciting program. You articulated last time where this has come from, who the audiences are, but we didn’t dig into the content piece and we left that for another discussion.

So today I wanna ask you about these major pathways and how we think about this and the opportunities and why this is so important and why teachers and others are responding so well to you when you’re presenting this.

[00:00:55] Mike: Thank you David. I’m Mike Obiero, I teach math at Maseno University. As you mentioned, I have different titles, but I like referring to myself as a math education enthusiast, and that’s basically where my interests are. And this program is one of the initiatives towards math education that we have. The other initiatives we’ve talked about, others we’ve not, but we’ll create time to discuss about them.

In the last episode, we talked about the origins of the idea behind the Math Innovations MSc program. It’s 10 years in the making, we’ve conceptualized it over a long period of time. I’ve had visits to the UK where I’ve talked to a group of statisticians trying to build the interest into contributing in different ways. I’ve had a discussion with my colleagues, acquaintances across Kenya, trying to figure out what their needs are.

The process of developing a program, you have to go through the needs assessment. You have to respond to the needs of people as you design the program. So, I’ve talked to lots of high school math teachers, I’ve talked to people in industry, I’ve talked to people just, you know, I’ve met in the streets in our towns, discuss about an MSc program we are envisioning, asking them what will work for them.

So for the high school teachers it was obvious, they wanted to continue with their academics and they’re looking for ways of doing that. Some people in industry were looking at ways to help them deal with data, which was a gap which is lacking, some people have data, they don’t know how to make sense out of it. And even yesterday, I was talking to my uncle who works at KALRO, this is the Kenya Agricultural Research Organization, and he was like, they have lots of data, but they’re not statisticians, they don’t know how to deal with the data. So there are people who just want to learn about aspects of handling data.

Then you have, artificial intelligence is the new, I wouldn’t say elephant in the room, but we are dealing in an era where AI is central to most things and people have to rethink whatever they do, whether you are a student, whether you are a teacher, whether you are working in industry, you are impacted with AI in different ways.

So as we were designing the program, we wanted to address, or at least meet, the demands of these different groups. That led to the different pathways of the program.

[00:03:30] David: Can I just come in on this because I think this is really important. There was an element that you mentioned in the last episode, which I think is worth repeating, that particularly when you were discussing with teachers, they often didn’t know which direction they wanted to go. They were interested in all of these things. They might be interested in leaving education to become a data scientist or they might be interested in becoming a modeller, or they might be interested in staying in education and becoming a really imaginative teacher.

And they had these many different ideas and they didn’t know which to go. And we mentioned AIMS, the African Institute of Mathematical Sciences, who have this broad MSc, originally this postgraduate diploma, exactly enabling people to be prepared for their next steps, whether it is industry or academia. And that’s a big part of the inspiration, which I think is worth drawing out at this point, that AIMS has successfully run this Pan-African MSc in mathematical sciences, this broad subject, enabling top students to have this exposure to this breadth of the subject and then find directions to pursue.

[00:04:46] Mike: Yeah.

[00:04:47] David: And that’s part of the inspiration as well, that you could be taking a narrow aspect in creating a program related to that narrow aspect, but your vision is to say, look, we need a program for teachers, we need a program, which is not many programs, and you are not wanting to compete with the existing programs.

[00:05:08] Mike: No.

[00:05:09] David: Everything which you are conceiving is broadly filling in these gaps and allowing people to find their way, and it might even be as is the case with AIMS, that after you do the MSc, you might want to do a specialist MSc, your second MSc, to become a real specialist in an area. Or you might be able to go straight on to research, or you might be ready to go out into the real world. But this is enabling you to sort of come out working where you are and to find your direction.

[00:05:44] Mike: Yeah. And I think you put everything into context and explained that extremely well. Most people going to do, especially for teachers, it’s not clear the direction they want to take. In industry, it might be different, that I want to get this skill to be able to do this. But most teachers, it’s not clear, some will want to proceed with their MSc in mathematics, others will want to maybe do something in education, others want to become data scientists, others want to do modelling, as you put it. It’s not clear to them.

And having a program which has only one pathway is very restricting, people’s interests change. So the way the program is designed is that the first year exposes you to different aspects, everyone will have to do something about maybe teaching, everyone will have to do something about, a little bit about data, everyone does something about maybe responsible AI or modelling. So you’re exposed to different things.

And from the way you interact with the course you might be like, oh, I wanted to do pure math, but I really like modelling. And then you’re like, I want to continue with modelling because now it’s very interesting for me. Or you are a teacher who wants to continue with education, but you realize you really like dealing with data, so your interests change.

We designed the program so that a student is exposed to different things and you don’t lose anything if, say, you take an education module and you are going to do data. It’s still going to help you at your place of work because as part of working with a team, you have to do a bit of mentorship. And so that’s equipping you with the skills that you need to mentor other people at your place of work. Or you might have taken a data course and you continue taking education. So all these are enriching the student in different ways, but they’re allowing you to narrow down on exactly where your interest is so that your efforts are now focused on that particular interest.

[00:08:00] David: Can I just come in, because you mentioned this sort of first year being very broad and one of the real advantages of the open university and working with them on this is they’ve actually designed this so that that is an exit pathway. You can get your postgraduate diploma in maths innovation just doing the equivalent of a first year. Of course it can take many years ’cause it’s part-time, but you know, you have an exit just at that, which would be if you want, the equivalent of the original AIMS program.

[00:08:31] Mike: I think that’s something unique with Open University, like most of their programs have these exits. Even the BSc programs, you can take a number of courses, you get a certificate, take a number of courses, you get a diploma, take a number of courses, you get the BSc. The same is true for most MSc programs, that you can get these different certifications leading towards your MSc. Again, that’s very appealing to most people I’ve talked to, because at some point, having a diploma helps as you move towards your MSc, yes.

[00:09:05] David: And as we know, we mentioned this even in the last episode, in other universities, there are many students who get stuck at the thesis of the project level who would’ve been better off exiting at the postgraduate diploma. This is a fantastic conceptualization from the Open University, really full marks to them for sort of thinking this through in the way they have, and it’s very exciting to have this program conceived in that way.

But I want to dig into the specializations in the second year. And you don’t have to specialize, but there’s sort of three broad pathways that I’ve heard, correct me if I’m wrong. There’s a pathway which relates to education, which you’ve said yourself, this is what you are really excited about, it’s this innovation in maths education. This is where your heart is.

[00:09:51] Mike: This is where my heart is, absolutely.

[00:09:53] David: Then the second one I’ve heard is the data one. And you’ve mentioned both, you know, data skills, but you’ve also mentioned responsible AI or AI and how you do that. And that’s another pathway which has elements of that.

[00:10:05] Mike: Yes.

[00:10:06] David: And that’s something which as we know, in society, this is widely needed. There are statistics degrees, there are data science degrees, but there’s this element on, you know, the real application side, this is what distinguishes these skills and thinking about AI in responsible ways. That’s what sort of distinguishes that from what already exists.

[00:10:29] Mike: Right.

[00:10:30] David: And then you’ve kind of hinted at the fact that, well, you might still want to become a mathematician. And you’ve explicitly mentioned the fact that, you know, the program is enabling people to think about pure maths or about modelling. And so in some sense it is both pure and applied maths, but even there, which is filling in gaps that are not currently part of the MSc programs.

So my understanding is you’ve managed to sneak a bit of number theory in there, haven’t you? Which is your own area of PhD, you know, there’s areas which are not currently being taught within the Kenyan system, but which are actually very suited to a problem solving approach and so on.

And so there’s some quite imaginative mathematics pathways or components within that third component. So it’s broadly a educational innovation component, a mathematics component, and a data and AI component.

[00:11:25] Mike: Those are the three major pathways. So I’ll start talking about the math one and lead towards the education one because that’s where I want to spend a bit of time. So there are people want to continue with their math MScs and we were trying to envision the type of math that will be suitable for them. There’s some deep level areas of math but when you think about number theory, think about combinatorics it’s an area that doesn’t need a very deep level math, it could lead to deep level mathematics in itself, but you don’t need a very deep level background of mathematics to be able to do a decent thesis in number theory, for instance, or in combinatorics.

[00:12:10] David: Can I just come in on this for a second? Because there are probably non mathematicians in the audience.

[00:12:15] Mike: Right.

[00:12:16] David: So, the traditional maths, the classic maths, analysis, algebra, these big areas, which are well covered in Kenya, there’s a good history of that , that’s what the programs tend to focus on.

[00:12:30] Mike: Yeah.

[00:12:31] David: And these areas, there is an expectation that you have understood the undergraduate level well, and if I’m not mistaken, there is actually an explicit course, which is pretty loaded, which has pretty much the whole undergraduate mathematics, but done almost as a revision module to make sure students are really up to speed. Because the expectation is students going in that pathway may have spent a number of years since their undergraduate and may have lost some of that.

[00:12:57] Mike: Yes.

[00:12:58] David: But then the inspiration for you on this, it comes from your own experience in some sense. When we engaged, I deliberately shifted you actually, it was towards a problem that was almost combinatorial for your masters because that enabled you to do research level activity with the background you had, even though there were some gaps that we’d identified.

And then when you went into your PhD, you actually got this amazing opportunity in number theory, you know, it is a top 10 US university, so it was a tough environment for you, but you were able to succeed by taking this area of mathematics where you could actually focus in on a problem where you didn’t need really heavy background and you could compete with your peers.

[00:13:42] Mike: Exactly, you are absolutely right that, traditionally, we have a very big community of analysis researchers around, some of the best professors we have are either in analysis or algebra. These are areas that need you to be up to speed with very deep mathematical concepts all the way from undergraduate. And for someone who has taken a bit of a break from college mathematics, it’s a tall order for most students to get to speed.

[00:14:10] David: Not just for someone who’s taken a break, please tell a bit of the story of yourself when you went to Urbana-Champaign.

[00:14:17] Mike: Yeah, and it goes to my interest on the education side, and that’s why I’m so passionate about the education. Because I was top of my class in Kenya, most of the courses I did at Urbana Champagne I had done in Kenya, think of abstract algebra, think of complex analysis. And I really struggled in those courses because in Kenya when I was studying them, we didn’t go into depth, it was like we were just brushing over and the lecturer would be like, you’re done.

When I went to Champagne, now it was really digging into the aspects of analysis, digging into algebra. And it was a real struggle. But I was able to keep up. And this is where maybe being a bit passionate and keen with studying helps. But the area of math we envision for the program, as I mentioned, combinatorics, number theory, you don’t need deep level mathematics to be able to do these problems.

And even if you go through history, some of the people who did, or contributed knowledge in combinatorics and number theory were doing it as part-time hobbies, and they were able to contribute to deep level mathematics. So, students are able to still contribute to mathematics, but it’s not that deep level mathematics that traditionally we have.

[00:15:45] David: I want to articulate what you’re saying very explicitly, ’cause I remember one of the stories you told, and I’ve forgotten if it was complex analysis or another subject. At the end of the first week you were suddenly worried because everything you already knew had already been covered.

[00:16:01] Mike: That was abstract algebra and I went into the course extremely confident. This is a course I got a straight A, so I did it at undergraduate, got a straight A, did it at master’s, got a straight A. So I went into the course, extremely confident of myself and I was like, ah, this one of the courses that I’ll have an easy time. And at the end of the first week I was like, oh boy, I’m in trouble because everything I had done was covered in like two lessons and there were some new concepts being introduced. And so I was like, if this is the first week, what will happen for the next 12 weeks?

[00:16:37] David: And I think that the key is that it is the fact that some of your colleagues, they had already seen all of this at undergraduate in their previous studies. And this is what you mean, you know, it wasn’t that they were smarter than you, it’s just that their education had prepared them for this course better than your education had prepared you for that course.

[00:16:57] Mike: Exactly, exactly. And so, we have one course which is Foundations of Math, for people who want to do the math pathway, they’ll have to take this course. And it sort of combines aspects of the entire undergraduate program into one course. So we hope that this particular course will be able to prepare students to the math pathway.

[00:17:19] David: We gotta put it into perspective. I tried my best to prepare you before you went to Urbana Champagne. And you did amazingly. But if you want to enter that sort of international level of mathematics, that preparation has to start earlier. It has to come through undergraduate, it has to start at school probably, that there’s this extra depth. And that’s part of what the innovation you’re trying to bring in.

But, let’s not get sidetracked for that at the moment. What I’m hearing is that this mathematics component, you have a very concrete idea of how the nature of that mathematics will enable people who are naturally, as you were yourself, good mathematicians, to emerge from the program ready for further mathematics studies. That’s sort of really the end.

But you could also have people who come through, good mathematicians, but again, as you pointed out elsewhere, get interested in modelling, and sort of on the applied math side rather than, again, the whole range of applied mathematics. There’s a specific focus on saying, well, actually, if people want to contribute to these complex models, these modelling things that need to happen, that are needed for society, this industry level of modelling stuff, this is where the mathematics stream could prepare them for that as well. Those would be two key areas within the mathematics stream, is that right?

[00:18:46] Mike: That’s right. And for the modeling aspect, the focus is to lead towards computer-based modelling, because nowadays things are rarely done by hand. So we’ll have to build their capacities…

[00:18:58] David: I need to articulate this again. So in the East African context, there has been some really good work done on training modellers in the theoretical approaches to modelling that are used in disease models and so on. There’s actually a whole big stream of this. So what you are doing is you are distinguishing that sort of more theoretical models, which actually are, important and really based on differential equations behind them and so on, that differential equation modelling, from saying, well, okay, if you don’t go deeply into the differential equation stuff, but you use a computer to solve those equations and you actually focus on building the models and having the skills to build the models, that’s a different training which is needed. And that’s what you mean by this computer based modelling.

[00:19:52] Mike: Exactly. So it’s moving beyond the traditional applied math that’s done, which is very theoretical, done by hand, towards leveraging on technology, which is using computers basically to design models. So, again, there’s the aspect of you might be interested in the math pathway, but you want to contribute towards modelling and not just the mathematics.

I want to, again, quickly go through data because I’m not a data scientist and I’ve also been impacted by AI in different ways. Again, there’s a lot of interest in data science, and in Kenya you talk to any person, they’re like, I want to do either BSc in data science, I want to do a master’s in data science. And the problem is that there’s no context towards the particular program you are studying.

So the data science aspect of the program is tied to aspects of development. You are doing data science, but it’s tied to, if you’re in agriculture, it’s tied to dealing with data in the agriculture sector. Yesterday I was in a workshop on R-Instat basically playing around with agricultural data. And I was amazed with some of the analysis and the interpretation.

As I mentioned, I’m not a statistician, I’m not a data scientist, but I was able to appreciate some of the findings in the data doing experiments, different treatments, how to make sense. And I remember the facilitator, Roger, was like you want to be a detective, a data detective, trying to identify anomalies in data and interpreting that anomaly and presenting that to the audience.

So, the data science aspect of the program is to do exactly this. It is not theoretical, but very practical. You are doing something with the data you have at your place of work, you can be sort of a data detective, detect anomalies in the data or make sense of the data, and actually give interpretation, a sound interpretation of the data to an audience, or give recommendations.

[00:21:58] David: Maybe it’s worth me coming in on this because as you’ve said, you are not the driver behind this part, it’s your colleague, James Musyoka, and we’ve had many discussions, we’ve got a number of episodes with him on this podcast, and he is really deeply involved in this. And I could maybe have another episode with him and Lily who’s working with him on this to be able to discuss, well, what are they aiming for in this stream?

Of course, I’m very involved in the stream myself, and it is a stream where maybe the thing that I will focus on, you’ve mentioned this is data science for development, and I want to contrast this with what internationally is now being seen as data science, which is really data science for tech.

I am not saying that’s not needed in Kenya. Kenya has a thriving tech scene, and it does have a number of universities trying to offer this very machine learning focused data science courses. But there’s a much wider need for data science where you understand machine learning, you understand traditional statistics, but you focus on data, and as you say, being this data detective.

And we need many more people to have those skills. That’s the essence of this stream alongside the ideas of actually thinking about and being aware of how AI is coming into all this and how to responsibly integrate AI in the things that we do. That whole theme, as I say, is maybe worth me getting an episode with James and Lily, where we dig into what they’re trying to achieve there.

[00:23:41] Mike: Yeah. As I said, I’m not an expert. I just have some vague idea of what they want to achieve. They’re brilliant people in their own respect. I met Lily just two days ago, she was part of the workshop on R-Instat, an amazing scientist. So, yeah, it’s worth you having a discussion with them, digging into their thinking behind what they want to achieve with that particular pathway of the program.

And maybe, again, as I discuss with people in the field who are interested in data, I can better articulate the thinking behind the data science aspect of the program.

[00:24:16] David: Yeah.

[00:24:17] Mike: So, now to where my passion lies, education. As I mentioned, we have mostly high school teachers who are interested in an MSc. And most of them are kind of undecided on what will be suitable for them. So you talk to them about, you want to do an MSc, they’re like, yes, I want to do an MSc. You give them this range of options and they’re like, oh, I’m not so sure what I want to do. And then I discuss the education aspects of the program and they’re like, I think that fits perfectly with me.

[00:24:51] David: I know you have a hidden agenda. You mentioned in passing the sort of fact that you want to get lots of people through this program. And your hidden agenda, which of course, because I’m stating it is no longer hidden, is that your hope is that at the end of the core part of the course, most of them get attracted into this education component and they actually become this amazing, well, team, if you want, this amazing set of manpower looking to implement the competency-based curriculum, to innovate, to find ways to do this, to work collaboratively at that. You want hundreds, and you even mentioned at one point thousands of teachers working together on this. This is your dream.

[00:25:38] Mike: Exactly. In the long run, my aim is to develop a huge community of math education enthusiasts, thinking innovatively about ways of teaching not just math, but other STEM subjects. And there are so many things that someone will do to innovate in the teaching. And I’ll just mention some of them.

So, we started with the use of technology in the teaching. There are different ways you can use technology, that can be in the assessment of students, give them immediate feedback as part of the learning process. We’ve done a series of math camps where we introduce mathematical concepts through co-curricular activities, this could be games, it could be a place, different co-curricular aspects.

[00:26:26] David: Can I just check? When you say co-curricular, you mean extracurricular or things that go alongside? What are you thinking?

[00:26:33] Mike: They are things that go alongside the curriculum, they are things that are not even tied to the curriculum.

[00:26:38] David: I was gonna say, the math camps were always extracurricular.

[00:26:41] Mike: Yes, yes.

[00:26:42] David: And so there’s this combination of things which are extracurricular and things which are aligned to the curriculum. Is that what you mean by co-curricular?

[00:26:50] Mike: So, yes, that’s what I mean by co-curricular. So there’s the extracurricular aspects, which includes like games, playing cards is something that’s frowned upon in Kenya, for instance, because people are associated with gambling and things like that. But if you think about it, there’s very deep math that goes with even gambling. How do you increase your odds? How do you know that you’re losing and you need to stop? There’s a lot of deep mathematics, and through playing card games among other games, you are able to introduce very deep mathematics to students.

And we’ve found that these activities, they arouse students’ interest. And we have evidence of people who are not even interested in math, but having attended a math camp, interacted with activities that introduced math, they went ahead to be ambassadors, not just enjoy the activities, but actively present activities to their colleagues. And it’s just changed the entire mindset, some of them are working in organizations in Kenya that deal with math and math education.

[00:28:02] David: Can I just come in here again because one of the things which I think you are articulating is playful learning. There’s a lot of research on this globally happening right now and this idea that if you can engage students from a young age in playful learning, then the learning isn’t a chore, it’s something which is happening. And this is how they get really much further, much deeper.

You did mention that, you know, there’s maths in gambling, but the key point, and I still remember one of the very early math camps, a very nice colleague of ours, Mary Achieng, at the end of the math camp said, you know, I now feel ashamed because I used to confiscate cards from my students, and I now see that instead I should have been teaching them how to use games which have educational value.

So the point is that it’s not that everything has educational value, but a tool like a deck of cards can be a really powerful educational tool. And that’s, this is what it’s really about, turning, creating opportunities for learning through play.

[00:29:15] Mike: Yes. And just helping kids identify patterns through nature you can design playful activities that help students identify patterns that lead again to deep mathematics. So, getting teachers to a stage where they can appreciate using these different innovations to motivate students in class, I was in a math camp a few weeks ago, and we had teachers as part of that camp, we were introducing some of the math activities through play, and even teachers were not aware of the math that was being introduced. And at the end of it all, they were like, these a much better way of introducing these aspects for my class.

So getting teachers to a point where they can appreciate using innovative tools, that can be tools that don’t use technology, it could be the use of technology in the teaching, we feel that it’ll not only improve the teachers themselves, but it’ll improve the teaching process.

[00:30:17] David: And more than this, this is not just your initiative. The Kenyan education system has just shifted to competency-based curriculum, which is based on these same ideas.

[00:30:29] Mike: Exactly. And, it’s again a challenge that the government has, that it has shifted to the competency based, it was curriculum, but now it’s competency based education, where kids have to learn to develop their competencies, and there’s a lot of emphasis through learning through discovery.

And teachers are having a difficult time because traditional teaching is you have a concept you want to introduce, if it’s geometry, then you start with trigonometry and you jump into the content. But now there’s a lot of emphasis on students to discover these different aspects of geometry by themselves. And the curriculum is designed to bring this aspect and teachers are struggling a lot.

We feel that it is giving teachers an opportunity to learn on how they can deliver some of these aspects of the curriculum in different ways where the emphasis is on students discovering the math are not the math being presented to them.

There’s another aspect of education that I really want to touch on and I feel teachers can be an integral part of this process. And that’s development of the teaching resources, especially the textbook.

[00:31:50] David: This is a fantastic topic, but there’s no way you’re gonna finish this topic in the next few minutes. We need another episode for that because I know where you’re going on this because we’ve already had episodes about your textbook, your open textbook efforts. That deserves an entire episode in its own right.

[00:32:08] Mike: I just want give a flavour where as part of the curriculum, or the MSc program, there’s the component of teachers actively developing the teaching and learning resources. And this is one aspect where teachers have an opportunity to develop their own resources for teaching and learning.

[00:32:30] David: Not only their own resources, we need an episode to go into your vision this because I love it and it’s really exciting stuff. Let’s have a separate episode, which touches on that. Let’s see. if we can wrap up this episode, because you’ve touched on these incredible content pathways, these three related to the education, related to the mathematics itself, related to the data. Each pathway is filling a gap that current MSc programs in Kenya don’t fit.

[00:33:05] Mike: Yes

[00:33:06] David: And yet they’re tying together into a coherent whole, which is particularly suited to this Open University model of education for people who are already in work, primarily from your particular perspective, those who are teaching because they’re the people you really care about and who you really want to reach and to serve.

I hope that this articulation of that content might be something which, if teachers were to hear this again, maybe you will get somewhere towards your numbers, because I think this is what they are looking for. When I’ve had discussions with Kenyan teachers as well, and I don’t think it’s just Kenyan teachers, I’ve had discussions with teachers across the continent and beyond. And these ideas are ideas that I think could resonate much more widely than Kenya even.

So I’m excited that the fact that we’re developing, we are co-developing these content as open educational resources, means that other countries can pick up and use these resources, adapt them to their context. And once they’ve adapted to their context, who knows what collaborations could emerge when you now get teachers across different contexts all working in these innovative ways.

[00:34:25] Mike: And just to add on what you’ve mentioned, the MSc program we designed is not, I mean, it’s perfect for the Kenyan context, but I’d argue that anyone in any given context can benefit by taking it. So it’s not just for Kenya, but I’ll argue that it’s sort of an international MSc, because of the international collaboration in developing the program.

So it’s not just responding to the needs in Kenya, but it’s actually responding to the needs of teachers across Africa and even beyond. So my hope is that yes, we can have the aspect of different countries, maybe they want to customize the program to deal with specific aspects in the context, and that can lead to broader discussions and collaborations between the Open University and different countries.

I know that IDEMS has an MOU with the Open University in developing some of these resources, and I think that’s a brilliant idea, because now IDEMS can act as an intermediary if anyone wants to collaborate with the Open University. The easiest way might be just collaborating with IDEMS, because IDEMS has been an integral part in designing the program, in designing the courses, and eventually in delivering the program.

So, I think it’s an ideal partnership between academia and industry in developing a program that addresses the needs of academia and industry.

[00:35:58] David: Thank you for those kind words. But I know your real intention is to get international students on the program. [Laughs]. Anyway.

[00:36:08] Mike: I’m trying to convince a few guys from Ghana to do it.

[00:36:12] David: Okay. This has been great. We will have enough other episode at some point soon on this intersection with your textbooks, I promise.

[00:36:20] Mike: I look forward to that. We’ve not even discussed about the math camps. Most of the initiatives we’ve had were born out of the math camps. And the textbook is the current big idea that we are working on. So I look forward to having other discussions about that.

[00:36:34] David: Sounds great.

[00:36:36] Mike: Thank you so much, David, for having me.