Sean Tibor: Hello, and welcome to teaching Python. This is episode 97, breaking down the compsi class stigma. My name is Sean Tibor and I'm. Kelly Schuster-Paredes: A coder who teaches Kelly Schuster and I'm a teacher who codes. Sean Tibor: Then this week we have a special guest joining us. Sri Konderhi is joining us from New York City to be able to talk about compsite classrooms and a lot of the experiences that students go through. And we're going to have a discussion about how we can make some of those experiences richer, more fulfilling, and make it something that kids are excited and enthusiastic about joining in. So welcome Sri. It's great to have you here with us. Sri Kundurthy: Thank you. Thank you, Sean. It's great to be here. Sean Tibor: Yeah. So I guess we're just recording on the very first day of school for Kelly. So Kelly, do we need to send food, extra bottles of wine? How are you holding up? Kelly Schuster-Paredes: Today was a crazy day between still trying to get some books into the system and 6th graders. Very exciting 6th graders. And to top it off, my son in 6th grade. So that was a first for me. Trying not to make too much eye contact, like trying not to kiss him and cut his head. And I realized this morning when I walked into class that I didn't kiss him goodbye. And I was feeling like, what am I going to do? But I terrorized all his friends and sent pictures to all their mommy and they were so happy. And it's surreal that my child is in middle school and I'm going to be teaching him middle school in Python. And he actually did make some ASCII art the other day just to prove that he can do some Python before he gets in there with his kids. So, yes, I'm having a crazy day. Sean Tibor: Nice street. Have you started school yet for the fall? Are you about to start or you have a few days left? Sri Kundurthy: Yeah, we have a couple of weeks. I don't want to think about that too much. But Kelly, I feel that would be a pretty fun experience, having your son in class. Kelly Schuster-Paredes: Would you like your parents teaching you. Sri Kundurthy: A lot of the time? I have worked very closely with them in terms of education. I think my dad was my first computer science teacher back in third grade or so, and so I sort of got used to it. But I understand how it can be a little embarrassing. Sean Tibor: He'll just have to get used to it, I guess. So will you tell me? Kelly Schuster-Paredes: Right. He'll be fine. It'll be me. He'll be scarred for life. Sean Tibor: Well, why don't we jump right into the wins of the week then? Hopefully the start of school is a win of the week for you, Kelly. But before we talk, we're going to make sure you go first because that's kind of our fun. Having a guest on the show is getting them to go first on the wins of the week. So Shreeni wins this week inside of the classroom, so we won't talk about school because it's not here yet. But outside of the classroom, any wins to share? Sri Kundurthy: I'm trying to think. Not too much at the moment, to be honest. It's been a relatively slow week. I was on a trip for a bit. I had some trouble at the airport, but it wasn't nothing too serious, I would say. I'm curious to hear about you guys. Kelly Schuster-Paredes: So well, I have to say that we go back to July or June 15 when I saw you speak up, Euro Python, that was a huge win for me. I was like, yeah, I'm getting this guy. That was a couple of months ago. That was huge. Sri Kundurthy: Was that your first conference, July 15? Not my first, but definitely a little bit of a switch from what I had done before. And yeah, that was awesome. So I casually put my Twitter account on there, not expecting much, and after the conference, like 30 minutes later, I go on to Twitter and check and it's going crazy. There's just a bunch of people reaching out, and I'm really humbled by the support and the fact that I was able to reach educators like you as well, who were really part of the intended audience. That was a really great experience. So maybe not a win of the week, but a win of the past month for me, for sure. Kelly Schuster-Paredes: Very cool. Sean Tibor: All right, Kelly. Yes, go ahead. Kelly Schuster-Paredes: Today was the last day of Michael Kennedy Fast API course, which I didn't get to attend, so I'm very sad, but I do have the video that he's going to send, and that was a huge win. That okay. So I'm going to be honest. Poor Michael. I didn't hang all the way through. Like, I was cheating trying to type the code, and everybody else was pulling it from GitHub, and I was like, I want to type it so I can learn. It going way too fast. So I had to watch all the videos again. But I feel like, super proud, super amazed. I love watching him teach. He's just such an energetic, great teacher. So that was a huge win that I made it through. Monday, Wednesday, 4 hours of beanie MongoDB async awaits. Learning how to pull in stuff from a database or at least understanding a little bit. Not necessarily. I can't say I officially learned it, but at least I'm more aware I'm going to learn it later. Better. But it was really good. That was a huge win this week. It was cool. Understanding APIs. I was sitting there talking to him during the course. I was like, Darn you. Now that I know what's going on, as I'm waiting for my not to name aggregator of app sitting there spending. Obviously they're not using Fast API and they're just calling up these vendors, and it's just sitting there and waiting for the data to come. I was like, Somebody needs to talk to them. And he was laughing. He's like, yes. See what happens. So it was a really good one. Sean Tibor: Nice. That's definitely something you've been working towards for a while. And I think it'll be a useful skill as you start to want to build more things. To have those tools would be great. Kelly Schuster-Paredes: And you have any wins in this sale of a week? You had a busy week. Sean Tibor: It was a very busy week. I mean, there were definitely moments, though, where I had some technical breakthroughs and that was rewarding and exciting. But I think the biggest thing over the last week was closing out the summer with my intern and the other engineering interns that we had and just having the moment to reflect. And it was interesting to look at all that they accomplished and all that they did, and I'm just thrilled with their progress and it sounds like they had a fantastic experience and they're very excited about what they accomplished. And there's a book that I got from my intern called The Missing README, which has a really nice summary of that kind of skill set that you need to go from being a computer science student to being a professional software engineer. And it doesn't have all the depth that you would want, but it gives you everything from here's, how to do code reviews, to here's how to collaborate on software, here's how to avoid dependency, nightmares, and all these things that you'll need to learn regardless of the language that you're using. So I thought that was a pretty cool kind of way to wrap everything up, bring it all together and get my intern to start thinking about how she's going to take her skills from the classroom and the lab into the real world and build some stuff with it. Sri Kundurthy: That's awesome. I was just thinking about that title. I think it's really clever. The Missing Read me definitely check that book out at some point. Sean Tibor: Yeah, I'll put a link to it in the show notes. I read through it before I gave it to her and then I'm going to probably get my own copy because as much experience as I have in a lot of different areas, I've never really pulled it together as an engineer or professional engineer like I am now. Kelly Schuster-Paredes: Well, where are we? Let's get into this because I'm so excited and I've been holding off not talking and having great conversations with the tree. Sean Tibor: Why don't you kick us off, Kelly? Kelly Schuster-Paredes: Then I was watching EuroPython presented on the educational panel and I got lucky enough to get a free pass into EuroPython. I didn't get to see all the videos and there's so many people that I want to watch, but I did get the people at Euro Python organized it really well and they had like an educational kind of stream on. At least when we were watching, everyone was in the same room or something. Sri Kundurthy: They had it by track. It was really well organized. Kelly Schuster-Paredes: Yeah, so well organized because we know we have an educational track on Python, but this one was everyone was in the same room. Everyone was talking about education. So I was like, oh, flip to that one. And I got to hear Tree talk. And then we were talking about all the things that we talked about, about breaking down the comps class, and I was just blown away. And then when I found out he was still in high school, I called Tyler, and I was like, oh, my gosh, this kid is amazing. I can't believe I'm so excited. I can't wait to meet him. So there we are. That's all I know about them. Sean Tibor: Nice. So, Shree, can you give us a brief recap of your talk at your own Python and the subjects that you're interested in when it comes to education? Sri Kundurthy: Yeah, absolutely. I presented on, I believe, July 14 or 15th, and the original topic of the presentation was on how we could use specifically Python, but more generally, computer science in interdisciplinary educational fields. So I was defining that at that point as sort of taking computer science and seeing how we could use it in a physics class or a math class or even there's applications in history that you can find or art classes. And what I mentioned there was that right now, there's a lot of work being done to simply increase the accessibility of education, where only 51% of high schools in America right now, approximately, are teaching or even offering computer science courses. And that's a number that has to improve a lot. So some could argue that before we even bring out interdisciplinary computer science courses, we first need to start by increasing the accessibility of computer science courses in general. But I think that bringing attention to a problem like that and one that's less discussed can actually sort of push development in both regards, where talking about interdisciplinary education can get physics teachers and math teachers excited about computer science and pushing for changes and increases to computer science education and vice versa. And I sort of supported that with a little case study that I've done where I personally took a day in certain high schools scattered in different areas and taught a lesson in a certain discipline. So, for example, if it was a physics class and I taught a problem in chaos theory, the three body problem, where I gave a derivation of the differential equations and things like that. But the bulk of the lesson and what I think really matters the most here was focused on helping students, many of whom didn't have that CS background, understand Python simulation that I gave them, where it was a numerical integration of these unsolvable differential equations so that we could use Python to actually still understand how the three body system behaved. And the feedback that I got was really awesome. And obviously, it's not statistically significant or conclusive, since it was on a very small scale, but that human impact where you have students like, hey, I want to learn more about Python, or this makes me want to study physics in greater detail. This puts physics into greater context, since I want to major in computer science, but I've always been sort of interested in physics, and now I see how I can bring both the fields together. Those are some of the insights that I've gotten from that. So talking at Python really allowed me to reach, I think, professionals in education, but also other people, community members who have been interested in this. So I'm really glad that I got to reach you guys completely. By coincidence, Kelly was watching, and that was really awesome. And now I'm here able to talk to hopefully more education professionals as well as students about this area. But another focus in my talk was also on just the general status of computer science with an education, some of the walls that exist, some ideas as to how we can break them down, and some ideas as to the current steps that are being taken. So that's a broad and not very concise summary of what I talked about, but hopefully that gives you some additional context. And I think that right now it is publicly accessible, but it's part of like an eight hour stream or something like that. But I reached out to them, and they said that they slowly cut those down to was a 30 minutes presentation. So hopefully you'll see that within the coming few months. Kelly Schuster-Paredes: Yeah, I found the trick just to share at time. Yeah, just from this one, everybody right. But it was great. I think it's something that's really dear to Sean, and I'll speak for Sean. Sorry, I'm speaking for Sean. Our hearts is the fact that a lot of classes, a lot of teachers, we don't have the opportunity to extend computer science. We have nine weeks, which is not enough for us, but we still do a lot. And just getting into the classroom and finding ways to even if the code is already there and you're running in a colab we use colab, Google Collab document, running something in there for them to push a button just to make coding more visible is something that's really important to us. And I think having that conversation more and more is going to broaden the scope for us. At least, I'm hoping. Sean Tibor: Well, I think that we can maybe break this down into a few of those walls and barriers and stigma that you mentioned. I think the first one is really that computer science is kind of isolated, right. It's just about coding, right? And it's about solving math problems with code or something like that, that it doesn't have broad applicability to other areas. And I think that's where we've started to see as we bring it into other classrooms, it enriches the learning experience. You have any sort of experiences as you are a student, where teachers may be tried to bring in computer science or tried to collaborate with other teachers or other subject areas, and maybe it went well, maybe it didn't go well. Or did you have places where you tried to bring in computer science into a subject that you were learning to make it a better experience? That sort of stigma, I think is relatively easy to disprove but hard to implement. Right? Sri Kundurthy: Yeah, no, that's a great point and great question. So what I found was, in terms of educators bringing it into the classroom, it tends to be one directional where computer science teachers like to draw connections to other subjects. But I found that other teachers didn't necessarily have that. And I think that that touches on a much broader issue and one that we could go down the rabbit hole right now for, which is that everyone learns math, science and English history, but computer science is very much an elective right now. So I was looking at a report from, I believe, co.org, and what they said, which I think is quotable and makes a lot of sense, is they said that right now computer science is seen as though it's vocational, but computer science is in fact foundational. And right now that's not how it's seen in the school system. So it makes sense intuitively as to why a computer science teacher can find connections to other fields, whereas a physics teacher who may not have gone through a computer science education very rigorously, might not be able to forge those connections. So when you're a student and you're sitting in both classes, I think that being able to draw that connection really does help. And I know that I've loved to do it where when I'm in computer science class or I'm doing a computer science project, I like to think about how it can apply to physics and sometimes even the other way around. Kelly Schuster-Paredes: But yeah, I think that I'm processing science as vocational foundation. I love that. And I think it comes down to a lot of maybe a generational gap. And I know this sounds really bad, but thinking just from my experience as a teacher and I'm a biomager premed, did all of my math, did physics, everything, but I never had computer science course. I'm not necessarily an old teacher, but I've been around. Right? And so most people on our average are at that point. It's not until I don't even know when they started introducing computer science as a mandatory basic, like in the first two years. I think it's just recently. So even as an educator, you have never really experienced a computer science course. So then to think, oh, we're going to do computer science and add it into the curriculum. It might be a generational gap. We're still at the point where we're arguing with math teachers about being able to use calculators. No, you can't use a calculator because on the AP test, you can't use a calculator. So I think when that gap gets broken, it might come around thinking adding into that, adding into the question, have you seen maybe some educators that are willing to possibly learn or explore computer science, maybe at your school that are not in computer science? Like math teachers? Sri Kundurthy: Yeah. So generally. What tends to be the case is with a little pressure and obviously you guys can speak a lot more to this. But it's primarily on a state by state basis and also more broadly on a country by country basis. But depending on pressure from government. There's continuing education programs that are sort of implemented in different schools where what you mentioned. Where an older generation of teachers who haven't had even. Like a mandatory computer science class and really don't even know the fundamentals. Such teachers could or may be required to take certain courses to understand some computer science principles or just some computational thinking ideas. If not programming classes. So that's what I've, in my research found to be the case. But beyond that, it's just not an issue that's discussed very much, where people just aren't seeing the need for it at this point. Sean Tibor: Well, I think it's an interesting phenomenon. Right. Because, Kelly, as you mentioned, there's teachers in the workforce who, when they went through their education program, maybe calculators were brand new. It really was a game changer. I'm not even kidding. We have this whole spectrum of teachers out there with a tremendous amount of experience, but it also means that a new teacher going through and getting the same degree on paper right now is getting a completely different experience. Right. So your average 23 or 24 year old math teacher that graduates with a degree in mathematics or a degree in mathematics education has probably had a lot more exposure to computer science principles and technology integrated into their education program than someone who graduated 10, 15, 20, 30 years ago. Right. So we are definitely in this era where we have a wide spectrum of teachers out there who have had a lot of formative experiences that were wildly different then. In addition to that, I think teaching is kind of an interesting field as well, where if I were to go into the workforce, where I stay in the workforce, think about how much transformation has occurred in business right. Or in logistics and shipping and all these areas where whether I graduated five years ago or 35 years ago, I'm using technology as part of my job in a way that I never did before. Right. And so this whole idea of a teacher being able to come into the classroom and never having to upgrade their technology knowhow, or take a computer science class is kind of different than it is in a lot of other fields. Right. It's a different expectation. And I know that it depends on the school, the school board, the state, the country, but how many math teachers being told, no, you really do need to go learn computer science. Right. You need to be able to program, which I think kind of leads us to another stigma potentially around computer science and computer science education, which is what's really hard. Right. You have to be really smart to be a computer science person right, to be a coder, to use it. And so the number of very bright, brilliant teachers that I've interacted with that would be perfectly capable of learning computer science but have told me that I can't do that. Right. Or it's too hard, or you're a different kind of person. Kelly Schuster-Paredes: They like this row. Sean Tibor: Yeah. So I guess that's another stigma to tap into and discuss and dissect. Is computer science hard? Sri Kundurthy: Right? And that's a whole other rabbit hole. But when you think about it, I guess I could raise the scenario. Is calculus hard? Kelly Schuster-Paredes: It was calculated hard. Sri Kundurthy: Right. Sean Tibor: Is literature hard, social sciences hard? Right. These are all hard subjects. Right. Sri Kundurthy: But I think what's important is that what comes before calculus is precalculus algebra two, algebra one, geometry, whatever it is, you're building to that point. So, I mean, asking if computer science is hard, asking if math is hard. And I think that this sort of touches on what I said earlier, which is that starting earlier with students where from elementary school, students are able to take a computer science class or at the very least, understand certain principles of computational thinking. I was recently reading about a study that they'd done with students around the world who didn't have access to computers and resources that you traditionally used to program. Instead, they gave them sort of like power free computer science materials, like, I don't know, little puzzles on paper and certain questions posing different questions. So it was a computer science class without computers sort of curriculum that they were testing out. And it did have an impact where the students were then when they were then shown computers and shown programming materials, they did understand that better. So building to that point where computer science is a very advanced field is something that I think should be considered a lot more where right alongside math and science, students are starting out with computer science courses. I think that that will help in a number of ways, where, first of all, one interesting statistic that I came across is that 57% of parents actually believe that students in general need to be very smart to take computer science classes. But the vast majority of them still want their children to take computer science. So that disparity there where I want my kids to take it, but I'm scared that they might not do well because they're really smart. That can be broken down by starting early, I think. So maybe I sort of looped around with that question a bit. But to make people think that computer science isn't a very hard or restrictive subject, starting earlier would help. And of course, that comes with its own set of challenges that warrant further discussion. But I think that that should be the basis. Kelly Schuster-Paredes: Yeah, and I'll do a whole circular thing with you as well. So that seems to be a trend. Right? So we talked to Anaconda and we're talking about data science and bringing data science in early, not necessarily going in there and looking at graphs and trying to dissect large amount of information, but bringing it down to the skills that are applicable to a lower school student. And I think a lot of schools are trying to bring in that computer science. There was actually a talk, I remember it was a Twitter or LinkedIn conversation about Scratch or no Scratch. Should we be bringing in Scratch? Is Scratch actually the real deal? I would encourage a lot of educators to go up there. We brought in scratch. We brought in the Scratch like block based programming because we don't have computer science teachers to teach the program, but yet we're bringing in some sort of code. But is that really going into computational thinking and problem solving? So here's my question. Where did you start when you started coding? I know you mentioned earlier in pre talk that your dad was possibly your first teacher. What kind of coding did you start with and how did it come? Because for me, I'm so amazed. You're way above my pay grade of Python right now. And I'm just like, oh my gosh, I wish I had you as a student. You would be teaching me so much. And I love that. So where did you start computer science? Sri Kundurthy: I don't know about that. I mean, you guys have done a great job, I think. But I actually started with Scratch, and I got a really silly book when I was younger and I worked through that book, but that book was split into where the first half of Scratch and the second half was Python. And Scratch is great, the projects were fun, but I pretty quickly jumped over to Python. I would say I spent no more than like a month or two in Scratch before I became interested in the Python side of it, of that book. So I think that though, if I were to do that again, I would still start with Scratch first. That helped build that interest, and it was a lot less intimidating. One thing that's for sure is that when you first look at a terminal and when you first look at code, and unfortunately, this applies to a lot of people right now, it just looks so overwhelming. There's so many colors, there's so much text numbers mixed in. And Python it's very close to natural language, which is a great thing about it, that syntax is very straightforward, but it's still overwhelming. And Scratch does help take that edge off a bit, where you're just dragging and dropping blocks and it's a silly little cat dancing around on a stage. So, yeah, I think that starting out with Scratch was definitely the right step. And for me personally, and obviously there's institutions and groups of teachers and professionals doing research into this and trying to understand a more scientifically guided answer. But I think that for me personally, Scratch was the way to go. Although I did keep in mind the fact that the ultimate transition was towards text based programming with Python. So I jumped over pretty quickly. But Scratch was a great place to start for me. Kelly Schuster-Paredes: In what age? Because I know a lot of listers love to know, what age did you jump over to Python? Sri Kundurthy: So I started scratching, I believe, around third grade. And within a couple of months, I jumped over to Python and once again, it was simpler projects, working mostly out of a book. But what was more interesting was trying to explore on my own and embracing the red. When you get an error, trying to work through that, that was really foundational for me. And I think that that's applied to a lot of the things that I've done after that with trying to work through a problem instead of giving up, because I'm not going to accept defeat to a computer. Sean Tibor: Yeah, I think the other thing I was going to mention around this idea of computer science being hard is that any subject is hard if you can't make connections to it, if you can't relate to it or have some sort of connection. So you mentioned this feeling of exploration, right? Like being an explorer on the computer and finding new things and trying them out. Would we think about other subjects in other areas? I think that's where computer science has a chance to really shine, to become that foundational building block of learning, because we can use it in so many areas to enhance the understanding of both computer science and the other thing that we have it paired with, right? Like it's the peanut butter and jelly. They go together really nicely, right. So when I think about this, and I think about the way that we're using computer science and bringing it in earlier, what we really want to get to is a place where we're not ripping out pieces of curriculum from other areas and replacing them with computer science. We're using computer science to enhance that. So if you think about, like, data visualization, a lot of students are learning about how to make a graph and they have like, a piece of paper or like a photocopy graph paper that they are tasked to draw points on, and they have to draw the line and they have their ruler. And their pencil, and maybe they get fancy and they have multicolored pens, so they can have more than one plot on there. But then it usually stops. Like you turn in your sheet and you're like, Here, I'm done. Right. What about taking that to even something like Excel or Google Sheets and plotting it that way? And then the next step is, could we do this with a little bit of code and do some more with it? Or instead of doing five or ten data points on the piece of paper, could we plot 10,000? Right? Is that something that we could do and start to show that computer science is used to think about problems in a different way, to solve simple problems, but then scale that up to solve complex ones? Sri Kundurthy: Absolutely. Sean Tibor: Maybe that has some value. Sri Kundurthy: Yeah, like just taking it one step where we're starting off with what you've already been learning, making little plots of whatever it is, some data in your elementary school class. But then, hey, why don't we take that into, like you mentioned, maybe starting out with sheets, but later on going into like, matplotlib or something like that. And that should help forge a connection where students it really demystifies what computer science can be used for, where maybe their perception at that point is that a program is a huge block of code that you struggle to understand that's hiding behind the pretty visuals on your computer. But it turns out you can take what you're learning in math class and with a couple of lines of code, turn that into a pretty looking plot on your computer. So that's a helpful connection, which should make it so that, I think, also help out with students who feel discouraged. And that can apply to some of the groups that we've talked about who are underserved or feel as though they don't have a place in computer science for a variety of reasons that can help out with that. Kelly Schuster-Paredes: I think I'm just processing this, like thinking in the teacher hat on, here comes this massive graph, or this information, and the teacher throws it up on the board and it's like, okay, graph it, plot it out. I want you to do it by hand. Okay, how do we do that? And breaking down the situation. There's a lot of process things there. And I think what it comes down to is what you're saying would bring it into the classroom. But I think it's for most teachers who are not necessarily computer literate, it's about how do I get from something so beautiful in my field, whether it's a physics class or math class, and break it down to smaller chunks where that my students can see it in order to be applicable even for computer science. Right. So you have this outcome that's this big, and you say, okay, try to graph it by hand, or you can't. Or try to do something by hand, and you can't well, let's look at how we get a piece of it. So I think that might be a good look at it. Sri Kundurthy: Yeah, I don't have a question about scaling there that makes sense. Kelly Schuster-Paredes: Scaling. Yeah. I'm trying to think. You like physics, right? What's the best thing that you've seen or you've done or you've coded that really kind of went, oh, I get that concept that's been sort of airy up there. And you brought it down with code. Sri Kundurthy: Yeah. I mean, physics classes in high school, they tend to be focused a lot more on, like, mechanics, so how bodies move in the real world. But even in that, there's very complex scenarios primarily concerning differential equations. So one area in physics that I've always loved a lot, for many years and only in the past, I want to say like five, six, maybe seven years, I've actually had the resources to try and understand is something called chaos theory, which some people say is more of like a science fiction idea. And I think that it is useful to apply a term to it. But it essentially is differential equations that model dynamical systems that are very complex, or more importantly, they gave the appearance of randomness, but in fact, they are completely deterministic. So what that means is that maybe you'll watch something like a very classic example is the double pendulum. You'll see the motion of the double pendulum and it looks ridiculous, and you'll say, hey, that's random. There's no way you can predict how that moves. But there's pretty complex differential equations that can actually tell you how the double pendulum moves. So you can actually tell at any single time step where that double pendulum will be. And that's something where programming is actually very useful because the nature of these differential equations is that they're not solvable. So that means that you can't find, like, an analytical solution where you can explicitly write out how that function behaves. Instead, you have to numerically integrate. And numerical integration is a very complicated process. It's also like a very computationally heavy process. And that's a perfect place to apply computer science. So I was able to develop these different visualizations of how these systems behave through Python. That was one of the earlier examples of where I was able to tie in these different fields. And I think that being able to do that a couple of years ago really did sort of get the fire started for my eventual presentation and work with trying to marry computer science. And another field. Kelly Schuster-Paredes: I'm going to let you take that, Sean, because I keep thinking of the butterfly effect, and I'm just like I know. Sean Tibor: But I think it's a really good point because one of the things that you can do or that you're doing with solving problems like this is you're cutting out the boring stuff, right? So in theory, you could calculate all of that double pendulum motion numerically by hand, it would be terrible. It would take a ridiculously long amount of time, right? Maybe a lifetime. But the equations and the calculations can be performed by a computer in much, much faster time, right? Like almost real time. Like you could see the simulation. So that allows the student or the learner to have this experience of going from an idea or a hypothesis, some sort of concept that they want to learn, to seeing it proved in action, see the results of that calculation in a very short amount of time and that allows them to make the connection for the warning. I mean, if I just started writing this out by hand, by the time I actually finished, I'd have no idea what I actually started or set out. Sri Kundurthy: To do that right. Sean Tibor: So there are other areas where this is useful. The graphing problem is another good example, right. If the computer can graph 10,000 data points in a second or two and it takes me a month and a half, my learning experience is going to be a lot better using the computer then it will be by hand. So that's I think the areas where we can really make a difference in terms of the learning experience is letting the computer take the stuff that's boring and tedious and repetitive and handle that and allow us to connect. The learning experience is the part where you're going from that idea of that concept to the AHA moment to actually see it play out. Sri Kundurthy: Yeah, absolutely. And when you think about it, that's actually quite foundational as well. Isn't that why we use computers? To make our lives easier? Yes, but also to solve very complicated problems that are repetitive in nature or have a certain quality that really calls on the nature of these computers. And I think seeing that by example is going to be very helpful for students. So yeah, absolutely. Kelly Schuster-Paredes: So my biggest fear when we talk about all this is someone's going to go out there and package all this stuff up and just go, here's your curriculum, and it's going to get lost in translation. And I'm thinking that the other side is the mathematicians out there screaming. But if they don't know how to even attempt to do it by hand, which is true, how can you actually program it? So there's like these two necessary evils that are going to combat against our theory of hey, let's break down the walls, let's put physics, physics, computer science in the classroom. Is there any kind of solution in your mind of how we can get there? Because a mathematician is going to yell. Because I get thinking of two good friends of mine who I always joke around with and say I can code all those and I can do all those 70 problems for your students in 2 seconds. He's like, yes, but they're not going to learn the concepts. I'm like, well yeah, they have to because they have to be able to program it. So conundrum, what would you say? Sri Kundurthy: That's a great segue. I think coder who teaches, teacher who codes, and me. I think we all agree there and it's fun to dream and think about the potential of this scenario. But implementation can be very difficult for a number of reasons. And like you mentioned, simply packaging that into a sort of deliverable curriculum can take away from maybe the novelty of this idea. But also it can really water down what we're trying to achieve here. And I'm not exactly sure of how to deal with that. What I've looked into is that there are certain other countries that actually do mention this idea of interdisciplinary coursework. I believe Canada is actually one of them and some nations in Europe as well. But I haven't found much information as to the success of these programs. So yes, policy states that you need to have courses or you need to have math teachers talk about computer science for whatever frequency, but I haven't found information on whether or not it's actually happening. So that's a really solid point that you make. But one thing that I did mention in my Europe Python presentation is that something that would really help is for people in the industry to simply keep doing what they're doing but also taking a step further, which is to try and reach students. And one thing that I mentioned is there's content creators on pretty much every platform now and students who are increasingly active on these social media platforms. And if we can transition such platforms to something that's more for education or even have someone who's working in bioinformatics post a little tutorial on how they're sequencing genomes with simple Python functions, then suddenly you have a student who's maybe interested in biology and this pops up on their recommended that can help them form a connection. So it doesn't always need to be through the schools. But I haven't found much research on that idea at all. So what I'm making mention of right now is primarily rooted in, I think intuition and some experiences, but mostly opinion at this point. But I do want to hear your thoughts on that as well. Sean Tibor: I wanted to go back to something that Kelly brought up I think is kind of related to this, which is this whole concept of if they calculate the answer, it's cheating, right, that they are not getting something out of the learning. I think we need to change that stigma as well because the idea is that somehow the subject of computer science is so hard that nobody can do it, but yet it makes it like cheating when you do it to solve actual problems, right? These ideas are just banging against each other in my head because I think what most people outside of computer science don't understand is that the work in computer science is not the calculation. It's not running the code. It's thinking about the problem that I'm trying to solve and how am I going to solve it in whatever way meets my constraints. And so if we're thinking about what we really want kids to learn, we don't want them to learn the calculation steps, right. And how to mechanically follow this algorithm in their head of how to calculate the problem that we give them. Here's your algorithm. Follow these steps. Here's how you solve it. We want them to look at the problem that's given or identify a problem, think about how that problem really is structured, what the aspects of it, and then come up with the steps and the approach to solving the problem themselves. Right. And then the calculation part becomes relatively obvious for most people. Right. So what we've done is we've said we're flipping this around. The important part traditionally has been the calculation step. Here's the problem. We're going to teach you this method for solving this type of problem, and you have to follow these steps and calculate the answer, and we'll validate that you did it correctly. Right. And what we really need to do is get rid of the whole calculation step that can be done by the computer and really focus on the how do you identify the problem, how do you think about the problem? How do you identify a way to solve it? And then how do you verify that your solution is correct? Right. Like, how do you know that it's right? Not just by looking at the answer. Kelly Schuster-Paredes: It's a whole educational paradigm shift we've always given them, especially in math. Here's your triangle, here's the equation. Go plug in the points. And right there, third grade, even whatever grade, math, fourth grade, fifth grade, math. Take away the equation and say, how would you solve it? How could you solve it? Granted. That's obviously a mathematician higher level of thinking. But let a child or let a student go through and say. Well. I can take a ruler or I can take this. But that's like a huge educational shift that's been chugging from my time pre when I first started teaching of how can we break down the walls and we give students the answers of how to solve problems and then commend them on putting in a bunch of points into a pattern that we've already given to them where they're not seeking the pattern. I'm sure we would have all night, but if it wasn't a Friday night at 630, we want you back on the show. Sri Kundurthy: Yeah. Sean Tibor: I think the other stigma that I don't think we have time to get to and I want to talk about is definitely the idea of computer science is not for people like me. Right. Whatever like me means people who look like me, people who sound like me, people who think like me, people like me from where I'm from. That's another stigma as well that we need to really talk about. I think it would be a much larger conversation, maybe that's a separate episode on its own. But I think that's the last one that I had in mind to talk about tonight was really this idea of computer science being for people like me, whatever like me. Kelly Schuster-Paredes: That was a hard thing for me to get over to because it's not mine. Sri Kundurthy: Right. And that's a massive problem. But sort of going back to, I think the heart of our discussion earlier, one remark that I can try and make is that I think that between different subjects and different grade levels, and when you consider education as a whole, everyone's goal, I think at this point is the same. We're trying to show students how to think critically, and that's the goal in computer science, where we're trying to teach students how to solve problems, but that also ends up being the same goal in physics class as well as in math class. And we haven't even touched on humanities. But there as well, if you restructure the idea of a problem, they're really doing the same thing. They're thinking critically about historical events, they're drawing conclusions, they're drawing similarities between current trends and previous historical trends. So the reason that it makes sense to increase the prevalence of computer science within other coursework is simply for the fact that at its very heart, the goals are the exact same as other curriculum that we have right now. Sean Tibor: I think that's as good a place to end this conversation as any. That was really well stood. Kelly Schuster-Paredes: I was about to get into a whole new other conversation, but we'll let that lie. Sri Kundurthy: Thank you. Sean Tibor: Now I think that is exactly the point. You've nailed it. That really the goals are aligned. We just sometimes need to get out of our own way and work together on it. Kelly Schuster-Paredes: Absolutely. Sean Tibor: So, Sri, thank you for joining us today. It's a great conversation and I know we have a lot more that we can talk about and discuss, so I'd love to have you back on the show again in the future. Kelly, any final thoughts or upcoming events and activities? Kelly Schuster-Paredes: You know, I just posted on Twitter a good conversation and food for thought for anyone out there who wants to have this conversation is about the ten libraries that they found on Python that had some malware. And not necessarily that conversation, I don't want to talk about that, but the implication of teaching that safety and digital citizenship within the computer science classroom would be an interesting conversation for me. Like we say, oh, it's PyPy, you can download it, it's safe. And how do we start teaching about looking for things? Or where do we go when we're at home and we're going to go get a library from somewhere? And I think that would be a really interesting conversation. So if anyone has any experience on that. I would love them to reach out and have that conversation. Sean Tibor: That sounds great. Well, I think we'll end it here. Sri, thank you so much for joining us. Sri Kundurthy: Thank you, guys. Sean Tibor: We're going to put your contact information in the show notes, but where can people find you on Twitter if they want to connect with you and continue the conversation? Sri Kundurthy: Yeah, so I think my Twitter might actually also be in the show notes, but it's at Kundurthy, I believe. And if you want to look at some of the projects that I've done, then that's on GitHub, where it's my full name at Tributza and then Kundurthy my last name. I think that's also linked in the show notes, so that you can drop that in the notes. But thank you so much for having me. This is a really great conversation and I definitely look forward to being back with you guys to talk about more stuff like this. Kelly Schuster-Paredes: I have a feeling that you're going to be someone that people are going to want to reach out to and talk to. You definitely piqued my interest and I've had such a great time talking to you or with you. Sri Kundurthy: Thank you so much. Kelly Schuster-Paredes: Friday night. Sorry, late week. Sean Tibor: Yeah. So if you want to get in touch with Kelly or myself, I'm at SM Tibor on Twitter. Kelly is at Kelly Paradigm. We're at Teaching Python on Twitter and you can always contact us through our website if Twitter is not your thing at Teachingpython FM. And so I think that will do. Kelly Schuster-Paredes: It for this week. Sean Tibor: Oh, and we do have a new LinkedIn page which you can follow for post. Kelly is managing that for us because I can't manage anything this week, so I appreciate her taking that on. So for teaching. Python, this is Sean. Kelly Schuster-Paredes: Kelly. Bye.