Episode 31
Sean Tibor: [00:00:00] This episode of teaching Python is brought to you by real Python. That's right. We have a sponsor now. So stay tuned for more information about what real Python offers in the middle of the show. hello, and welcome to teaching Python. My name is Sean Tibor. I'm a coder who teaches. 
Kelly Paredes: [00:00:26] And my name's Kelly.
She used to print as, and I'm a teacher who coats 
Sean Tibor: [00:00:28] this is episode 31 preparing for the school of 2024 AI machine learning, VR and AR. That's a mouthful, 
Kelly Paredes: [00:00:35] huh? Don't forget data science, data size 
Sean Tibor: [00:00:39] development, you know, teaching problem solving skills, social emotional learning, all sorts of stuff.
Kelly Paredes: [00:00:45] Right. Wham. Bam. Then put in there. Yeah. This 
Sean Tibor: [00:00:48] is going to be a bit of a, you know, potentially a big synthesis episode where we're really trying to bring a lot of things together as we. Gaze into the crystal ball and try to predict the future. So Kelly, how are you doing today after having gone through all of these different technologies and learning styles and educational trends?
Kelly Paredes: [00:01:07] You know, I like research, you know, I'm that data person and I was, I was enjoying myself, but you know, we decided to record today after I, my research so I can get it all in. 
Sean Tibor: [00:01:17] Yeah. I think we need to capture some of your energy for this cause I know you're really excited about it and I am too.
So we'll start before we get to our main topic. We'll start with the win of the week. So my one of the week is, I mean, I have to admit it's, it's definitely a bit geeky or more than a bit geeky, but I printed a three D printed lightsaber over the past week and I finally got it working and it's really cool.
It slides out and everything. And, and my. I mean, there's two reasons why I made it. The first official reason is because there's always some kid at the end of the school year or at the end of the quarter that says, I want to make a light up light saber with all the Neo pixels and everything from a to fruit.
And I say, that's great. And then I never really have the right design or the right thing, and I'm trying to figure out how to print it for them. But now I've got it dialed in so I can print them a white Sabre. And the real reason is just because I think it's really cool. 
Kelly Paredes: [00:02:07] And I know I have to say with a sigh of relief.
I'm so glad you didn't take the real win of the week. We, for me and Sean, and for teaching Python, we're so excited. We have our first real sponsor that we're going to talk about later in this episode. 
Sean Tibor: [00:02:22] Pun intended. That's our first real sponsor. 
Kelly Paredes: [00:02:25] Uh, yes, totally pun real. A real Python. 
Sean Tibor: [00:02:29] A real Python is wiser.
Right? So exciting news. This episode is now sponsored by real Python. So stay tuned and we're going to hear a little bit more from them later on in the podcast about the services that they offer. Both Kelly and I are huge fans of what Dan Bader and his team are doing over at real Python. So it was a really great win for all of us to have real Python support the podcast and help us move forward.
So thanks to them and, and thanks for getting us started on the sponsorship route and we'll talk more about what real Python Haas has to offer later in the show. 
Kelly Paredes: [00:03:02] Excellent. So. This week's topic. 
Sean Tibor: [00:03:05] Yeah. I was like, what's the dive right in? Because I'm like, I'm excited about, let's just jump into it.
So what do you mean by the school of 2024 Kelly? 
Kelly Paredes: [00:03:12] You know, in our role, you know this, obviously they were kind of, that was kinda staged, but in our role, we are constantly having to look towards the future. We have to investigate products that are out there, products that are getting made and Kickstarter, things that.
We'll be on the horizon. We look at the horizon reports, we look at Pew reports, we try to find trends that we think are going to stay. And it is a tough job knowing if these trends are good or if they're just, you know, whims. But it's a fun job to get into. And one of our things we were talking about is what is it going to be like in five years?
Sean Tibor: [00:03:50] Right? And I think this is, this is really interesting because it is so layered. There's so many different ways that you can approach this. One way that you can look at this as just what are we going to teach, what subjects? What's going to be part of our curriculum for students in five years? So what are we teaching today that prepares them for five years from now?
What are we going to teach our students in five years? And that's just one layer. Another layer is how is our school going to operate in five years? Like how are we changing the way that we teach, the way that we administer our school, or the way that we manage things, the way that we think about how we get kids in the door to learn and how do we make sure that we're effectively teaching?
So there's so many different ways that you can look at this. And our goal with this topic is to really try to. Focus on a few things that we know are going to have some big impacts on the way that we approached teaching in five years. 
Kelly Paredes: [00:04:46] And I think, I think bottom line that we have to say is we know in computer science, we know with Python that that coding basics, that coding, that understanding of Python in the language, it's there.
It's predominant in machine learning. It's in the artificial intelligence route. But we also want to make sure that our kids can transfer that outside of our computer science, what we're teaching them in here, and make them understand that this thing, and we'll call it a thing that's around them, that's almost not seen, is actually this machine learning, this AI, this, this way of the future.
Sean Tibor: [00:05:25] That's right. And there's really, it's changing so quickly, right? So no matter what industry you're in. So it can be really difficult sometimes to bring all of these different areas together as it's happening so quickly. We know that there's so much change happening. There's so many different news sources to pay attention to.
So much work that's being done in this area that it's hard to synthesize all of this together. And do that on top of your day job.   you're trying to predict the future and deliver the president at the same time.  so what we tried to do, just to make some sense of it for ourselves and organize it, was we organized it around five trends or five themes that we saw happening in a school context.
That makes the most sense.  if we. Looked at the entire range of everything that's happening in machine learning. For example, we'd be here for the next three days talking about it, and by the time we finished recording it, it would be different. Right? So what we want to do is just focus on this in a school setting.
So Kelly, what's the first trend that you saw when you were going through the research? 
Kelly Paredes: [00:06:25] Well, I think maybe a trend, but more just looking at our role first and comp sigh and how we got here. Right? So we've been looking at the different types of machine learning and how it Progressed and how many years when it's been 
Sean Tibor: [00:06:38] 40 50 years, right?
Where there's been any, there's been an explosion in the last five or 10 because the computing capability , has caught up. But if you look at a lot of the early machine learning research, I mean, you've got papers, mathematics, papers as far back as the 18 hundreds that are describing neural networks, and then you've got more recent research, like at bell labs in the 1940s and 50s that were the first.
Primitive attempts at this. But the reason why we're seeing so much rapid change right now is because of Moore's law. It's finally enabled computers and computing to have enough capacity and speed to be able to handle these large complex problem sets. 
Kelly Paredes: [00:07:17] And, and I think it was when we were talking about AI, and we went and we had some of our, not necessarily colleagues, but we had a group of our colleagues and we had the guys from FAU come and talk to us and train us about neural networks.
 is when we started to notice more of it coming into the comp computer science classroom more into the data science. Looking at ways that while you're in computer science say, Oh, this is a gateway into machine learning. This is a gateway into facial recognition. This is a gateway and how we're going to analyze data.
And I think that trend is now. More evident and more schools. 
Sean Tibor: [00:07:55] Right. So I think the trend here is that in order to take advantage of all these other aspects of computer science, of technology, of where the classrooms and schools are going over the next five years, you have to establish basic literacy in coding.
So you have to understand. How code works. You have to understand how to think through problems the same way a computer scientist does, right? How to solve forms, how to compute answers. So computer science, literacy, coding literacy is trend number one. We need to make that as pervasive as possible.
For our students and actually for our administrators, right. What does it say about us as a school? If we tell our students that computer science is really important, everyone should learn how to code, but none of the teachers know how to code. 
Kelly Paredes: [00:08:45] Exactly right. And also understand that whole evolution of the machine learning, that the process of going from the rule based.
All the way into the neural network. I think just maybe we don't have to be able to explain it or the kids don't have to know it as deeply. I mean, I'm not an expert in it, but just understanding that there has been a definite change and there are things that are rule-based and then there are things that we are pumping in the data to learn.
Right? So that's going to be, I think, the two trends of, of in our classroom and other computer science classrooms, and 
Sean Tibor: [00:09:16] also being able to recognize, again, if you understand the difference between the two and you start to see. Rule-based inference engines coming into play or rule-based logic engines coming into play and you recognize the limitations of that versus something that is a trained model.
Making inferences or making a decisions, you'll be able to know where and how to best focus your energy and know how to use the tools that are available to you. 
Kelly Paredes: [00:09:40] Yes. So another area that I think is really easy for us to do, a nice prediction of trend is in the other classrooms, just looking at how we can use a AI VR AR in order to get students more familiar.
And one of the things that I know that we do, but not enough and we would like to do more, is really bringing in our systems of the VR systems and the AR systems in order to help students go around the world and travel or to be in someone else's shoes. 
Sean Tibor: [00:10:14] Right. I think when you think about VR in particular, right?
And AR. I haven't really thought through as well, to be honest. But, but when it comes to VR, the purpose of VR is to be able to shift your perspective and change your perspective. And that's really a critical thing for students to do, for all learners to do, to change your perspective and understand how that shift in perception and perspective changes your output or changes your understanding of a situation.
And. When you think about VR and AR, think about it on a few different dimensions, the perspectives that you can change. You can change your spacial perspective. So I can go from being on the ground here in Florida to being on the ground in Pompei and I can tour Pompei and look around the grounds, the ruins of Pompei without actually leaving my classroom.
Right? So before, if you wanted to do that, you could look at pictures. Which sort of changed your perspective or gave you a unique perspective on it, but actually hopping on an airplane and flying to Pompei test, walk around the ruins and see it for yourself and smell it and experience the sights and sounds of it is the best way to change your perspective until we now have VR.
So now with VR, you can get a reasonable approximation of that experience by putting on a headset. The second perspective to change is scale. So you can change your scale, not just in location, the geospatial shift, but you can also change the, the scale and the perspective from being very, very small and to being very, very large.
So some of the most interesting VR applications are the ones that happen at a microscopic level where you can swim through a cell wall when you're learning biology. Or fly around the source system or look at other planets when you're looking and studying astronomy. Right? So these applications let you see things at a larger or smaller perspective than what you are right now.
Kelly Paredes: [00:12:10] And don't forget social emotional learning. You know, one of my favorite talking points, the empathy building, we sat through professional development the other day talking about how students are starting to lose that. Social recognition, trying losing that face to face understanding of emotions.
While I remember last year we set in and we looked through the Stanford 
Sean Tibor: [00:12:32] homeless becoming homeless, 
Kelly Paredes: [00:12:33] coming homeless. That is such a scary, that is such a scary and enlightening experience to be able to sit in someone else's shoes and experience what it is like. To be homeless, to not have a place to go, to be hungry, to constantly have to watch, watch your belongings while you're trying to sleep, and just imagine the feeling.
And that was a good, and that was a new, relatively new VR experience that came out, 
Sean Tibor: [00:13:02] right? So I would categorize that as the human dimension, right? Being able to shift your perspective and put yourself in another person's shoes. So the empathy dimension is what VR also enables. And it may seem a little counterintuitive that if we want to get better at reading each other, that we put this thing on our face and and block what we, what they can see of us, but in combination with other.
Experiences. The ability to shift your perspective, to see how other people see things from their perspective is a really important dimensional shift. And then the last one that I'm gonna bring up is similar to the spatial, right? It's the time shift. So being able to shift back and see things the way they used to be.
So one of the things that I'm still trying to check out, and I don't think they have a VR version of this, but the one of the more recent Assassin's creed video games, it's set in ancient Egypt has a tourism mode. Where you can go on a curated tour of all the environments that were created to make an entertainment product.
And it talked about all of the different archeological and historical research that they had done. To build this world of ancient Egypt and try to make it as historically accurate, as soon as possible in an entertainment product. And they even go into the parts where they can talk about, you know, what things they are not historically accurate because of, you know, entertainment reasons and what it actually would have looked like.
So those sorts of dimensions let you start to think about, now, how could I use this in other classrooms if I'm shifting empathy? Am I really thinking about. You know, English as my, you know, oral language, literature type, classroom setting for that shift in perspective, if I'm changing scale, is that science, if I'm changing, you know, spatial location is that, is that geography, is that these, you know, understanding other cultures and building empathy in those places.
So once you start to think about what can I, what are the knobs in the Weber's, I can turn with this. Technology product, then you can start to think about now if I change this, where is it a good fit outside of the computer science classroom and in the rest of the school? 
Kelly Paredes: [00:15:11] And I'm going to not talk further on that cause I would love to continue to talk on 
Sean Tibor: [00:15:15] a separate episode just about that.
Kelly Paredes: [00:15:18] And let's just shift gears. I mean, in the classroom, I think there's going to be more talks about the ethics, more talks about, you know, how do we get rid of the bias. And development and machine learning or AI. How do we make sure that the choices that we make are actually ours? Or are these algorithms actually ruling our lives and to make the students and even the other teachers understand that we have a choice.
We just have to make sure that we look at the underlying bias that may have resulted or may have driven that choice. That is not that we're now seeing on the computer screens. And I think. There's got to be more of an education side about where this came from and, and how these computer programs and models have been made.
Sean Tibor: [00:16:10] Right. I think really. You know, I don't agree that the best thing to do is to eliminate bias. I think the best thing we can do over the next five years is get better at teaching and better at understanding where bias comes from. Understanding, being able to quantify or estimate the amount of bias that's inherent in something, and understand how that affects the output.
So we may not be able to eliminate bias in what we're doing and sometimes biases there intentionally for good reasons. Okay. And, and that's, that's okay. But unintentional unknown bias is what we're trying to fight against. We want to make sure we understand it and we know how to either compensate for it or adjust to reduce the effect of it if that outcome that is giving is undesirable.
Kelly Paredes: [00:16:56] Exactly. And let's go on a little bit larger scale. Let's look at how, how these trends are affecting students going on along the line with these bias and ethics. They are now a lot of apps out there that. Can actually help do homework. What are teachers going to do that are going to help them facilitate information better?
So just take Socratic back in 2013 a group, I guess they have two people, three people. I don't really know the history of it. They developed an app called Socratic, and it has now become an app that most students. Can turn to in order to find answers on homework that they can't find on Google.
Because let's, let's be honest with ourselves, when a student has a problem, they don't contact their teacher. They're gonna either go to Google, which we encourage in our, in our classroom, or they're going to get on a chat and ask a friend or a parent. But now you can actually take a snapshot.
Send it to the app and it comes back with a problem solved or an answer given. So more of things like this where this machine learning, or even a group of people in the back end, I'm not really sure exactly how they get into it, but they're going to answer questions for, for students. So how do we change our, our classrooms so that the students.
Can see the learning benefit. 
Sean Tibor: [00:18:16] Well, and I think you have to look at the motivation to write. So if a student is getting just hammered by their parents to get good grades, get good grades, get good rates, all the time, they feel this pressure that they have to perform, that the grade is the most important thing.
Of course, they're going to take a shortcut. Of course, they're going to go to an app like Socratic. Of course, they're going to try to find a way to get the grade if they don't understand the material, right. Or they don't have the skill built. So there's. With all of these, there's more to it than just how do we solve the technology, or what do we do in the classroom, or how do we change our teaching styles?
There are so many different influences on why students behave the way they do, why teachers behave the way they do, right? Why administrators behave, they do. So in this scenario, if you're sitting there thinking yourself, there's so much more to it than that. You're absolutely right. There is so much more to it than just saying, you know, how do we.
How do we prevent this from happening, or how do we stop it or how do we redirect it or whatever. There are so many things going on at the same time, and sometimes the tool is just the pressure valve that gets, that releases it, or the thing that we see. Rather than the real cause of what's happening.
Kelly Paredes: [00:19:25] Absolutely. And how about foreign languages? I love foreign languages. I wish I spoke tons of them. I try to, you know, speak Spanish and I, I give a couple of my words in German, but that's about it. And, and, and in what, 2016 Google translate had currently 103 languages. And in the past, the, the translation was horrible.
You know, it would be a word toward direct translation, but as times gone by, this machine learning has been analyzing more and more documents in order to provide a better translation system. 
Sean Tibor: [00:20:02] Yeah. I think anyone who's used an actual translation dictionary is not surprised by this. Right. The fact that when you use a, you know, English to Spanish dictionary.
Oh, it tells you is, here's what this word means in the other word. And you know, it doesn't tell you all of the nuance and structure and you know the, the cultural baggage associated with each of those words. It tells you the definition and the closest approximation to that word in the other language.
So when we look at how machine learning has transformed this, it's trying to bring in all of that other context, all that other. Nuance and information and and richness to the language and turn that into a better way to translate. And what I find interesting about this is that how these tie together, so now we're starting to see products that have come to the market, like Google's earbuds that they have, the wireless headphones that they have.
We'll do direct translation. If someone's speaking to you in one language, it's going to pipe the translation of that into your ear bud in your language. And so we're, I don't think we're at the point yet where we can say we're going to, you know, stop teaching world languages, right? I think if someone says that to you, you should really look hard at them and say, do you really understand why people learn world languages is not just to be able to do translation?
And it's not to be able to say. We're going to teach you how to speak or ask for nachos or a drink, or where the bathrooms are, how to find LA Biblioteca. Right? Right. Like, I'm actually not that good at learning other languages. But the point of learning a world language is not just to learn how to speak or write another language, it's to learn culture.
It's to learn how to relate to other people and how to understand them in a language that maybe you're not as familiar with. And that's the part where we can go further and say, look, Google may get really good at translating the written or the spoken language, but what we still have to work on is how do we.
Understand each other. How do we understand our culture back and forth? How do we learn more about other people through the way that we communicate with 
Kelly Paredes: [00:22:20] them? You just say emotional, social learning. What? I think that's, I mean, I think that's a fair point and I really think that could be a great trend.
How can we use translation to identify the way that someone would speak this sentence? How would we say this sentence? What would a person in Peru. Look like while they're saying this sentence, I would be interesting to, you know, our, our, what we say is not everything that we really say, right?
It's our, it's our body language. It's our facial expression. It's the way that we, we hold ourselves. So how could you use that trend in order to make a positive contribution to your classroom? 
Sean Tibor: [00:23:03] This episode of teaching Python is brought to you by real Python. That's right. We have our first sponsor, and we're really excited that it's real Python because we use real Python all the time for our own learning and for teaching students.
Real Python offers tutorials, video lessons, interactive quizzes and challenges, and the thing that I really want to dig into and I haven't yet. I don't know why because it sounds amazing, are these learning pathways that give you the opportunity to dig into a topic like writing more Python at code across multiple lessons.
Real Python is an entire learning platform that helps you grow your knowledge of Python and problem solving in a really robust way. We use it with students all the time. Just last week we were doing it following a tutorial with open CV and face detection that are seventh and eighth grade students were doing pretty much on their own, 13 and 14 year old students that were learning how to use face detection with Python in code.
And it was pretty cool to see their faces light up when it recognized faces for the first time in their photos. So. Go check out real Python. They've got monthly and annual subscriptions that give you access to all of the content on the entire platform as well as a private Slack channel for other learners like you.
So give it a try. You can find them@realpython.com and I'll put a link in the show notes as a sponsor so that you can check it out. Also, now, back to the show. Right? So we've talked about each of these in the context of kind of where they are today and generally about where we think they're going.
But if we put a number on these, right? If we start to think about for educators, for administrators, how do we start to think ahead? So in five years, where is this going? Let's start with a Google translate. Let's assume that Google translate gets five years better. At translating between different languages, right?
There's amazing things when they bought world lens, it can translate. Live video, you know, you can put it up on a street sign, it'll translate it into your local language and even overlay it on the image. It's amazing. Microsoft has translation tools there. They have machine learning vision towards that.
What you look at a scene and tell you what's in that scene. So you could combine that together and have it tell you in your language what the street signs say and who's walking around and all of those things. In five years, we get five years further along that path. We get closer to that perfect universal translator, like a star Trek style thing where you not only understand what someone's saying, but their intent behind it, why they're saying it, that the emotion that they convey with it.
So now what do we do as educators and in a school setting? What does that open up for us? Now? We have the ability to get even more global than we are now. Right? Especially as as Americans. We have a tendency towards ethno centricity where we look at, you know, everyone should speak English, right?
And because so many people do already, it's the language of aviation. It's the language of business. It's often the language of technology, 
Kelly Paredes: [00:26:00] right? You're coding 
Sean Tibor: [00:26:01] in many cases, the language of coding, right? When we spoke with Ruben learner on last week's episode, he was telling us about how he will teach classes in Hebrew, and the slides will be in English, and that's totally normal, right?
But. Even with that ethnocentric perspective and with the fact that there are so many people who do in fact speak English, whether it's a second, third, or primary language, whatever it is. Having the ability to empathize and communicate with people across language barriers in an even more effective way is going to be empowering for students.
We are going to be able to access more of the world's population and more of the world's population will have voices that reach the rest of the world. When it doesn't have to be translated by someone else, right. Or it gets locked away behind the language. Those are the things that are really interesting to me in terms of five years from now, where could we be in terms of the way that we use translation to communicate more effectively and what does that open up for us?
Kelly Paredes: [00:27:04] It's extremely interesting, but we're going to, we're going to go even further. Let's go. Let's keep going on that educator path. Let's, let's think about things that are actually going to. Possibly change or are changing right now in in the world for educators, you know our job, we talked about in automate, the boring teaching stuff.
There are things that computer science and Python can do for us. Let's go a little further. What are the things that machine learning can do for us, and they're already these automatic graders. We're going to go out and we're going to have an automated essay score. It's. Currently what in 2016 or 2017 one of the newest, most advanced machine learners program out there, we put an essay in and while law, you have a grade, can you imagine four for these English teachers?
Instead of spending two weeks grading an assignment, you know, we throw it in and we kind of scan it and say, Oh yeah, that looks about right. Yeah. How does that change? Just imagining how that's going to make our lives better? Is it going to make learning better? You know, that's the questions and the trends that we have to think about.
Sean Tibor: [00:28:11] Well, you know, even more so because I think that there are plenty of English teachers who would argue and say. No, I really do want to read the essays. I want to understand what my students are saying for a variety of really good reasons. So maybe they reject that whole idea of automatic essay grading, right.
For their own classrooms. But think about like the AP tests, for example, all of the essays that are read and scored and everything like that. We're basically just using human intelligence. To try to come up with the most standard way we can to assess student knowledge on the 
Kelly Paredes: [00:28:46] AP and how much time is, you know, how much of human error happens?
Sean Tibor: [00:28:49] How much biases in that system, right? So now you take someone who may be doing their best as a greater. Right. And they're very, very consistent about it, and they're following all the rules and they're doing it the right way and everything like that, and the person next to them is phoning it in, right?
Or they just really don't know what they're doing. Or they're, they have their own inherent bias in the system. Now all of a sudden we can try to reduce that, right? So I see that coming next, and I know that that's disruptive because a lot of business models are based on this, right? A lot of standardized testing is based on the ability to have fair and impartial grading of essays and of long form content.
But is it truly unbiased? Is it truly fair? How much cultural bias do we have in the essays that we write? How much implicit bias do we have in the essays that we write and the essays that we grade? So I, for one thing, that the really good benefit of automated essay grading is really around making it fair, right?
Making grading of essays more fair to more students. Not just across classrooms, but across school systems and across countries around the world to make sure that we're all on a level playing field. 
Kelly Paredes: [00:30:04] Let's even take it back even before an not so sophisticated machine learning program, like an adaptive learning platform.
We have mango high. And our school kids get math correct. They get a harder problem. It gets, you know, they can't get the question. It goes to an easier problem. Same thing that happened with a couple of standardized tests that we used to use in the independent schools. It had this adaptive learning model so that you can get a ruler base.
Of where that student is at every single point of the test. It's a remarkable piece of data. Instead of those standardized tests where everyone gets the same exact test, everyone either gets all right or all wrong and gets a number of value, but how do they know that that one question was a glitch and and just using these simple adaptive learning platforms in order to differentiate the learning and to make sure that all students.
Learn at their level, 
Sean Tibor: [00:30:59] right? And then optimize, right? So the ability to maybe start slower and then ramp up faster later based on the data science behind it. The predictive modeling that you can do. And here's the thing, if you're skeptical about this, if you think like, Oh, you know, maybe that's a, we're not there yet.
It's like, this is done in video games all the time. So video games have adaptive difficulty. All the time. They scale things up and down without the player ever noticing it. If you've ever played Mario kart with a four year old, right? The four year old does pretty well, and he feels really good about his playing.
Right, and that's all adaptive difficulty, right? It helps him out when he's falling behind. If he starts to get ahead too much or he's winning all the time, it makes it harder. So these sorts of these sorts of systems are out there. We just need to apply them in a learning context 
Kelly Paredes: [00:31:48] and let you know, and I had this thought, think about Gmail right now.
Let's do another switch. Let's go even to a different, I don't even know what type of machine learning this is. You'll have to help me out that way. But if Gmail can do predictive text, why can't Google docs? When is that going to roll out? So that as I'm writing my essay, you're starting to predict what my sentence is going to be and how can that be adapted and to help us write better, more efficiently.
Maybe we want to build out our lesson plans, not even imagine what the students can do with it, but as an educator, just being able to, to get the information out of my brain and have adaptive or predictive texts helping me. 
Sean Tibor: [00:32:26] Right? I think it really comes down to what do we really, what are we really assessing.
Right. What do we really want students to demonstrate for us as educators if we want to use this so that we as educators are writing better? Shouldn't we teach our students how to use the same tools so that their writing better and that their ideas, their understanding is developed faster? It's this whole philosophy that students shouldn't be limited by the tools or the language or something like that, or even their own learning differences, right?
Yeah. That the tools can help them better connect their ideas and their understanding and communicate that to a, an educator and give us better tools for assessing that and evaluating what truly matters. Not just do they use correct punctuation. 
Kelly Paredes: [00:33:15] And now let's just, let's go back. Let's go and just bring it out to the whole school.
And I'm going to throw in the, the silliest part first because this is something that I love, that we have in our classroom, but let's just look at the fact of cleaning. Right? We have an, which should we not even say the brand, but we have a, a robot that vacuums our floor right. It is the start of some sort of automated process.
Some, what does it, machine learning in there. Some a little bit visual recognition, 
Sean Tibor: [00:33:44] you know, it has to map the room. It has to do some things where it adapts to the environment that it's in. 
Kelly Paredes: [00:33:49] So I can see a more of a trend for some automated services happening in the classroom. Right. 
Sean Tibor: [00:33:55] And, and I know what you may be thinking, well, why do you need a robot vacuum?
Right? We have janitorial services. Everybody has a janitor. We're not trying to reduce the role of the janitor, but. We have a lot of traffic going in and out of our classroom, and in the middle of the day, who's cleaning up the classroom? It's the teachers. It's not the janitor, right? There's nobody coming in to clean it.
So having the robot vacuum means that the floor is clean during lunchtime when no one's in here, or while we're working in here. So this way we're reducing teacher workload. You know, another one that I'm really excited about in terms of reducing teacher workload, it kind of goes into that automate the boring teaching stuff.
I haven't an Amazon deep lens now and I, one of the things that I want to do with the deep lens is use it to do automatic attendance. And so if you're not familiar with it, Amazon's deep lens is a camera and a computer mashed together. So it's a high resolution camera and it's a reasonably powerful computer, basically in Intel computer in there that runs you been to, and you can run Amazon.
Image recognition on there. You can run video detection, you can run all sorts of stuff. So this deep lens, you can run Amazon image recognition on it. Video recognition. You can do face detection, object detection, all of these things. But what I want to do with it and what I think is coming is the ability to have face recognition in the classroom to detect when students come in and are attend class so that student teachers aren't remembering to take attendance every day and we know where our students are.
The interesting thing about this is that. The way that this is set up, you don't actually have to send any of the video or the pictures off of the device to anywhere else to be able to recognize the faces and it builds a mathematical model and then sends a mathematical model of the face off to be recognized.
And that's what. Preserve some of the privacy of students. We're not sending pictures of students off to Amazon's quad to be recognized. And that part's pretty cool. 
Kelly Paredes: [00:35:48] Yeah, that's really, it is really cool. And I saw you playing around with it the other day, and then it was something that kids like, Oh no, I don't want to be on the, on facial recognition.
So that leads us into privacy. I think there's gonna be more of a trend with, as a whole school. Talking with all of the constituents, parents included of how we are using the data, where is our data going? I know Europe made that huge switch about data and the rights and the processes. What are we doing here?
Sean Tibor: [00:36:17] Right? Because I think it's not that we're going to use any less data. It's not like we're going to suddenly collect less data about our students or about our teachers or about how we're processing information. We're going to be collecting more. And so really it turns into how do we use that data and how do we use it in a way that's safe and protects our students?
That's the only reason I bring this device into the classroom is because one, I have control over where that data is going and how it's being used. And two, there's a way to do it without having to entrust the most sensitive data to somewhere someone else. And in fact, you can take lessons from other categories like.
When you're doing double blind studies, every patient has a unique identifier that can only be decrypted somewhere else. So if we use an outside service, do we use anonymized identifiers for our students so that the only place we can de anonymize them is in our, in our school where we can then protect the data to the best of our abilities.
Kelly Paredes: [00:37:14] And last, I think it was last year, we had a great conversation with some of our higher ups. About data and Alexa and Google and how some teachers are bringing these devices into their classrooms and where are those voices being stored? We talked about if I'm bringing in my Alexa from home and I'm connecting it to my phone, then everything that happens with my Alexa with.
With my phone is recorded there. And that can lead to a whole bunch of negative opportunities that we had to discuss. You know, we like to assume that's not going to happen in our school, but you never know where, where, where does that go? So we had a great discussion about how are we gonna do Alexa for business?
Or are we gonna use Google or are we going to use it on a, on a school? Mandated device and it was a great conversation just to have, and this is talking in 2018 so I think more trends on that. Even those little things of what we're going to do 
Sean Tibor: [00:38:19] with, right? The more devices we put in the classroom, the more of those are going to have cameras and microphones and sensors that can record the environment around us.
How do we ensure that we're taking the appropriate steps to protect student, teacher, parent, all of these stakeholders? How do we protect their privacy and use their data. In the most sensible way possible. 
Kelly Paredes: [00:38:37] Let's turn data though to a positive, because I love, you know, I'm going to say it again.
I love data. I would love to see a way that we can take all of our data and to pro, not necessarily profile because I don't want to profile him too much, but profile a student. How can we use the data in a positive way? And there are some places where they're using data. When they collect on the students testings, on the students' backgrounds, on the students' attendance, and they're using them as early warning prediction systems.
There's schools, a couple of schools in universities where they are looking to see if they're on track here, the on track indicators for these students so they can go in and get a printout of where they are, where they should be, what grades they need to get, so that they can guide their.
Educational journey better. And I think that form of data, using it in a positive way to predict outcomes is going to be a good thing. 
Sean Tibor: [00:39:33] Yeah. And I think it has to be used in a smart way. So we can't go back to the way that we looked at, say, standardized testing to predict success and predict outcomes.
Right. There are probably just as many studies that have shown that standardized testing doesn't predict success in life as there are standardized tests out there. Right. So. We have to look at this in a richer way. This is a place where we have to look beyond just what we can easily measure and into the things that are harder to measure and look into the things that are maybe harder to quantify.
So yes, there are ways that we can quantify. All of these things are ways that we can measure them, but how do we make the right decisions. Over the next five years and in five years time to be able to say, this is the right information that helps truly predict success, and we have to define what that success looks like also, but this lets us truly predict success rather than just gives us a score that we use for maybe shortsighted purposes.
Absolutely. 
Kelly Paredes: [00:40:34] And I'm going to throw out the curriculum thing for the whole school with all of these trends and all of these projections, I think there's going to be. A bigger look in 2024 at different courses. We know that, you know, least in our hearts as computer science teachers that this data science and analytics and math is really important.
And where are we putting that information into our curriculum? Are we going to be able to change our schedules and our subject areas in order to add. More of these fields so that, not that the students can become programmers and coders and machine learning developers, but so that they can have a better awareness of the world.
That's. Changing around 
Sean Tibor: [00:41:21] them and how do we make, take the best parts of each of these subject areas, the parts that really help grow students into the kinds of adults and leaders that we want them to be. How do we help bring the best of both of these areas together? One of the things that we need more of in computer science is discussions of ethics and morals, and not just can we do this, but should we do this?
Or what's the way that, you know, that helps the most people. Right? Those are the questions that we should be bringing in from the humanities to computer science. It's not just about exporting technology to the classroom, it's about importing and sharing and cross pollinating across all these different subject areas so that we get to the true learning, the things that help us grow our understanding, both as students.
But also as educators, as administrators, as parents, so that we're all in moving in the same direction. We're all focused on the same things because ultimately we all want the same outcomes for our students. We want them to be grown up, happy, healthy, well-adjusted, thoughtful. Yeah. Upstanding people of good character that are going out and accomplishing amazing things for our society.
And the only way that we get there is if we all work together and we bring all of these things together. So I, one of my big trends that I see in 2024 and I really sincerely hope we get there, is this idea of collaboration and sharing taken to the next level where we're really working together, we're blending the learning across disciplines.
It's less siloed and nothing is walled off. Everything can be worked in collaboration with other subject areas and mixed together. And one of my favorite things is blending history and technology together. Study of the past and where we've been to where we, where we're going is fascinating and some of the stuff that I saw at PSI the summer was right in line with that.
We're, we're taking artificial intelligence, machine learning models and using it to reconstruct pottery from 10,000 years ago. That's an amazing way to use technology to further our understanding of where we came from as as people. It's really, really cool. 
Kelly Paredes: [00:43:23] Remind me too, to show you Ken Robinson.
Okay. About just the changing of the educational paradigms. He's been saying it for a while and we're still saying it. I think a lot of educators are still saying it of when are though curriculum shifts going to happen. 
Sean Tibor: [00:43:38] Right? Like when, right. 
Kelly Paredes: [00:43:41] And so I think Whitney into any 24 
Sean Tibor: [00:43:43] no, let's try to make it happen before that.
Right? 
Kelly Paredes: [00:43:45] So there so much more that we can talk about, but we want to hear your thoughts. Yeah. This is a little bit different than Python. We know, like in our hearts, it's Python really in the world. Anyways, so we wanted to have that conversation and interested to hear what your thoughts are.
Sean Tibor: [00:44:01] Yeah, it's, it's really, we're in an interesting position, especially as a Python community, that many of the most exciting things that are happening that will be here in 2024 are being written in Python code 
Kelly Paredes: [00:44:12] today. The Pew research center released the report October 28th we'll post that on our show links.
That's a really interesting read if you have a couple hours to look at it. And also the Rand corporation, they released January, 2019 and another report. These are the things I like to read. 
Sean Tibor: [00:44:31] So like reading for Kelly, want to talk about what's happening in 2024 I know the, the thing that we're excited about is that.
Yes, there are a lot of potential concerns. There are a lot of things that we have to be cautious about when we think ahead to the future and everything, and it's real easy to get caught up in those. But there's also a lot of really exciting, positive change that's possible with the things that we're seeing today.
And this is a really exciting time to be a teacher. It's a really exciting time to be a student. And. You know, I feel like, at least for me, one of the reasons why I became a teacher was it was the opportunity to give students an education that I didn't get to have when I was a kid. Right? So if someone was out there in the mid nineties saying, what's the, you know, adults of 2020 look like, what are they, what skills do they need to possess?
If someone had said, well, they should know Python, and everyone said, what's that? But when they should have something that says. What are the skills, what are the behaviors, the traits, the characteristics of those successful adults need to have, and how do we help give them those skills? Now, those are the things that I wish I had.
And so I think about 2024 but I also think about 2034 and 2044 and where are we going? What are the things that are durable skills that we can teach our kids now our students now they can take with them into the future. And those are the things that I get really excited about as a teacher. 
Kelly Paredes: [00:45:53] Yeah.
Can you imagine what our five-year-olds. We both have five-year-olds, so where are they going to be? Graduate 
Sean Tibor: [00:45:59] was the one that blew my mind was the fact that my, my five year old will probably never have to learn how to drive a car. 
Kelly Paredes: [00:46:04] Yeah, we could go on for teaching Python. This is Kelly and this is Shawn signing off.
Sean Tibor: [00:46:11] This episode of teaching Python was brought to you by real PI's on check them out@realpython.com