Become a Creator today!Start creating today - Share your story with the world!
Start for free
00:00:00
00:00:01
Gamification of Education with RISC - Ep 181
292 Plays3 years ago
MEMBERS CHECK YOUR BONUS PAGE FOR A GREAT BONUS SEGMENT!
We talk a lot about how to get archaeologists to learn more outside the field of archaeology in order to better understand archaeology. On this episode we talk to JD Calvelli from the University of Chicago's Center for Radical Innovation and Social Change (RISC) about gamifying the learning of data science. However, this isn't for adults; it's for 4th-6th graders! Adults are able to learn as well, though. Learn something new and do it in a fun way.
Connect with James on Twitter: @paleoimaging
For rough transcripts of this episode go to https://www.archpodnet.com/archaeotech/181
Links
- Center for Radical Innovation and Social Change
- Algo-Rhythm
- https://apps.apple.com/us/app/algo-rhythm/id1623193311
- https://play.google.com/store/apps/details?id=com.EnableEducation.risc2
Contact
- Chris Webster
- Twitter: @archeowebby
- Email: chris@archaeologypodcastnetwork.com
- Paul Zimmerman
- Twitter: @lugal
- Email: paul@lugal.com
ArchPodNet
- APN Website: https://www.archpodnet.com
- APN on Facebook: https://www.facebook.com/archpodnet
- APN on Twitter: https://www.twitter.com/archpodnet
- APN on Instagram: https://www.instagram.com/archpodnet
- Tee Public Store
Affiliates
Recommended
Transcript
Introduction to Episode 181
00:00:01
Speaker
You're listening to the Archaeology Podcast Network. Hello and welcome to the Archaeotech Podcast, episode 181. I'm your host, Chris Webster, with my co-host, Paul Zimmerman. Today we discuss the gamification of education with J.D. Calvelli. Let's get to it.
00:00:22
Speaker
Welcome to the show, everyone. Paul, how are you doing? I am still doing okay. The project that we've mentioned before of going off to the field again has been pushed back by another week. I think that means I get an extra recording with you in addition to this week's, which is exciting. I'm looking forward to everything that we can do before I go. Last week, I kind of
00:00:42
Speaker
fell into a project that's pretty cool. It's a mapping project with an historical group out of Yorktown Heights in New York, and they're trying to find the locations of Rochambeaux, a number of Rochambeaux camps from 1781 and 1782 when the French army was here as part of the American Revolutionary War.
00:01:03
Speaker
I'm doing this as a volunteer, but again, it's that intersection of archaeology and technology and public education and all these things that mean a lot to me. Nice. Nice. That's pretty cool. How are you doing, Chris? Where are you? I am in, well, technically Ocean City, but the bigger town is Ocean Shores, Washington. It's over on the Pacific coast. Well, kind of central Washington coast, I guess, so to speak, right on the Pacific Ocean. In fact, there's a trail.
00:01:29
Speaker
15 minutes to my right as I'm sitting here is the Pacific Ocean. It's all cloudy and crazy out there. Nice. Good weather. It's just started raining and it's cloudy, so not so much. But you like that. I do actually love it. It's not so good for our tech setup in here because our solar panels on the roof.
00:01:46
Speaker
We were up in the Washington peninsula for the week before this, and I had to run the generator every day because our 1800 Watts of solar on the roof was not enough to keep the batteries topped off. We were maybe getting five, 600 Watts of peak solar during the day. And then it would just trail off on both sides of that. And, uh, that's just not enough to, to keep everything going. So had to run the generator. This park rat has, has plugins. So am I hearing this right? It's, it's raining in the Pacific Northwest. It's crazy, right? Yeah.
00:02:17
Speaker
Speaking of a crazy world, let's talk about gamifying education. And we are bringing on
Guest Introduction: JD Calvelli
00:02:23
Speaker
a guest. I actually, I get random emails from people saying, Hey, this person might be good for an interview. And to be honest, most people just see network or they see podcasts and they send it out without thinking. And I get these just like totally off the wall requests for interviews for people. And this was kind of one of those things I thought, and I was looking at it and I was like, wait a minute.
00:02:43
Speaker
I was just like almost ready to hit delete. And I was like, actually, this sounds kind of interesting. And I think we could make this work for this podcast. And I really want to talk to this guy. So let me tell you first a little bit about JD Calvelli. JD Calvelli is an analyst and UX designer at the Center for Radical Innovation for Social Change at the University of Chicago, otherwise known as RISC.
00:03:02
Speaker
He is a recent graduate from Brown University, where he studied political theory and modern culture and media with an emphasis on the impact of emerging interactive technologies. At the Center for Risk, JD has been working primarily in the education space, finding new and interesting ways to modernize and expand what it means for teachers to teach and students to learn.
The Center for Radical Innovation for Social Change
00:03:22
Speaker
JD, welcome to the show. Hi. Thanks so much. It's really, really awesome to be here. Super excited to speak to you and the rest of your listeners.
00:03:28
Speaker
Fantastic. Well, why don't we just start by learning a little bit about the very well-named and makes me want to work there Center for Radical Innovation and Social Change at the University of Chicago. What kind of stuff are you guys doing there? Yeah, for sure. So the center of risk is we affectionately short-handedly call us.
00:03:46
Speaker
So it's somewhere between a social innovation lab, a think tank, and a nonprofit. It sort of sits in this interesting space in between a bunch of different areas of thought and of knowledge. So we're affiliated with the university, but we don't really have the same incentive structures as like a university department would.
00:04:08
Speaker
or university research would. We aren't a for-profit enterprise. We don't have the same incentive structure of having to appeal to a profit motive or profit incentive. We are a non-profit, but we are a traditional non-profit in the sense that we don't necessarily have to apply for grants or anything like that. Luckily enough, we're able to be funded by generous donations, which is really awesome. We're started by Steve Leavitt, the author of Freakonomics.
00:04:32
Speaker
Sort of because he did a lot of work, right? He thought a lot about how the world's an interesting place and we don't always look at it from the right angles. We depend a lot on conventional wisdom to kind of guide the way in which we approach different elements of the world. And he was sort of like, well,
00:04:50
Speaker
I don't know about that. It might be interesting to sort of take an outsider's opinion or an outside perspective sort of unencumbered and unfettered by these other incentive structures to see what's really going on in some of these spaces and try to do some genuine good. So that's the long and the short of it. It's a very new organization, so we're still kind of figuring out where we are and what we do. But we have a lot of different projects in a lot of different spaces, one of which I'm here to talk to you all about today, I imagine.
00:05:18
Speaker
Yeah. And I need to find out more about this. I do think that that where you're just saying looking at the world from multiple angles, that's something that we're constantly trying to remind ourselves. And that's why your project and risk were appealing to Christen me. Because even though this isn't about archaeology or directly applicable to archaeology, that
00:05:38
Speaker
willingness to step back outside of our comfort zone, outside of our normal perspectives, talk to somebody that doesn't do what we do to try to find out where these intersections of culture and technology intersect with what we do try to do and view the world, view our work, view our interpretations from multiple angles. That's a really big thing.
00:06:00
Speaker
Before we jump into it, Chris, I don't know if you recall, but back in November of 2017, we interviewed Joshua Fairfield at episode 67. It was a similar sort of thing. He was talking about his book owned. It had nothing to do with archeology, but it had a lot to do with technology and the ownership of technology and such.
00:06:21
Speaker
That was a nice intersection. I keep on thinking about that book and thinking about that discussion that we had because it's resonated over these last few years and I'm hoping that some of what we talk about with JD today does the same.
Collaboration with Steve Levitt
00:06:36
Speaker
Yeah, absolutely. And first off, JD, you kind of had me at Steve Levitt because
00:06:42
Speaker
I listened to a lot of Archeology Podcast Network podcasts as a producer, and I've kind of had to stop listening to some others. But I used to be a pretty religious listener of Freakonomics, and any time they'd bring on Steve Levitt, I was just like, oh my God, this guy, he's just speaking everything that is right. Because he just is so kind of like deadpan and matter-of-fact with his opinions. It's just amazing. I love it.
00:07:04
Speaker
Yeah, he's a really interesting guy. And our office is really small. We're only about 16 analysts. So we get to work directly with him. And he's just amazing. He's super intelligent, really interested in, like I said, challenging conventional wisdom, thinking about how we could think about certain things differently, try to find the truth hidden in the details. And I hope some of that has rubbed off on me in my time with Risk. And we'll continue to going forward.
00:07:31
Speaker
Yeah, absolutely. So we're going to talk about one of the things that you wanted to talk about on the show, but I'm just wondering, do you guys as analysts, are you working on a number of projects right now or are you super hyper focused on a project with a team?
Diverse Projects at the Center
00:07:46
Speaker
How does that work?
00:07:47
Speaker
Yeah, so it sort of depends, but in general, we tend to cycle around on a bunch of different projects. So I've had the benefit of working in a bunch of different areas, although mostly my interest area has kind of driven me into the space of education and modernizing education and how we can sort of improve educational outcomes. But some other sort of projects that I've worked on thus far at risk have been working with a company that has developed an algorithm to predict
00:08:17
Speaker
earthquakes, which were previously considered to be unpredictable. So we're working with them to build out an API to validate the extent to which they actually are able to predict earthquakes. Another project that I worked on was with a group out of New York that was interested in
00:08:36
Speaker
utilizing beatbox for speech therapy. We work with them to develop a randomly controlled trial to test whether their curriculum actually does benefit students with speech impediments. The spaces where people maybe aren't necessarily looking to find social good, the spaces where there isn't so much attention drawn, we try to look.
00:09:07
Speaker
You use the word analysts and not researchers, and that is interesting to me. There's got to be a backstory to that. Yeah, I think not sure for the backstory, but I think a lot of it comes down to a lot of people that come to risk, come with a more data analytics background. And so that was partially where I came to risk from. I was political theory with applied political theory lens, which is kind of like an oxymoron, I think, if you think about it.
00:09:21
Speaker
OK, nice. I'm going to actually at the risk of derailing it, which is my main function on this podcast.
00:09:35
Speaker
So a lot of my work in political theory was with the application side, the data analytics side there. And I think a lot of other people come to risk with a similar background in terms of approaching problems from a data-driven perspective and trying to suss out some interesting information from data sets and whatnot. And that's Steve's big thing. Steve is a huge fan of
00:09:59
Speaker
taking data and looking at data and thinking about how, oh, well, what is this data actually showing us or what is this data actually telling us, as opposed to what we think or what we assume it would be telling us. So that's, I think, where the analyst term comes from. But we do our fair bit of researching as well.
00:10:15
Speaker
Well, I would assume I was just a little hung
Exploring Gamification in Education
00:10:18
Speaker
up on that title as being not what I expected. And I just thought maybe that's looking at the world from different angles and give yourself a title that's not necessarily what one would expect in a research environment. Yeah. That's kind of what, that's eating your own dog food to a certain extent.
00:10:38
Speaker
All right, so probably in segment two, we're going to talk about the game you guys have developed or are developing called Algorhythm. And before we get there, though, I've got the title of this podcast. Hopefully I don't change it after this recording.
00:10:51
Speaker
of this podcast. And in the intro, we're talking about the gamification of education. So as you guys are looking around and identifying places that need help, that you can do something for, as you mentioned, what made you land on gamification as a way to help education? Because there's probably lots of ways we can try to help education since it's incredibly flawed in a lot of ways.
00:11:14
Speaker
I think there's a lot of ways in which education needs help and we hope that it can be helped in every possible way that it can be. We decided to take a look at the gamification space, particularly because it's new enough where it's interesting, but it's not established enough yet where it's really well understood exactly
00:11:35
Speaker
what it means in our opinion to like gamify education. It's very hot, right? I think a lot of people kind of like, oh yes, let's, let's gamify everything. Let's talk about how we can make everything into a game because it will incentivize people to do things. But it's like the, the exact ways in which, in which that's going to work in the education space is still very much on the up and up in terms of how that's going to.
00:11:56
Speaker
to happen. So we found that an interesting space. Also, Steve, as an academic, is very interested not only in data analytics, but also behavioral economics. And he's often described as a behavioral economist. And while I was not a behavioral economics major, I've been surrounded by individuals who know far more about it than me. And one of the most important things about behavioral economics, and one of the most interesting things about behavioral economics, at least to me in my time at risk so far, has been about incentives and incentive structures.
00:12:25
Speaker
And I've used that word incentive I think like a hundred times at this point already, and it's only been like 10 minutes, but I think it will come up a lot more as we continue to talk. But the idea behind incentives and nudges of like, how do we get people to do things that they otherwise don't necessarily want to do?
00:12:44
Speaker
And education and gamification of education kind of spits in that mental model of like, okay, in some cases, kids don't necessarily want to engage in education or the education system as it currently exists. Can we utilize gamification? Can we utilize, you know, games and interactive art and experiences like that to try to incentivize students to engage and with things that otherwise they might not be interested in engaging in and hopefully spark an interest in those things going forward.
00:13:12
Speaker
even if they didn't think that they would have an interest in them. So that's sort of, I think, why we decided to look at the gamification angle specifically. Yeah. And that's tough with education too, right? Because it's not just education as a buzzword, it's education of certain topics. And I'm just thinking about like normal, normal games and specifically like video game type stuff. I mean, a lot of the things that
00:13:36
Speaker
stick with people are some of the more harder tasks, but still entertaining in a way that makes you want to do it over and over and over and over again to either try to better your time or, you know, just do better at it. And then something just sticks in your brain. Obviously, if you do that thing through repetition and trying to get people to do that and also teach them something valuable, not just how to shoot and kill aliens in a more efficient way is a huge challenge I can imagine.
00:14:01
Speaker
Yeah, absolutely. And I think the way in which we've been thinking about it. So I come from a game design background. I studied sort of interactive art as well and game design prior to coming to RISC. I was kind of an odd duck, I think, for RISC before getting there, which is cool because I came with a different perspective and a different mindset, which I think is exactly what Steve's looking for in terms of people showing up, you know?
00:14:25
Speaker
You know, one of the things, one of the things about that problem sort of is a fundamental game design problem of like, how do you design a mechanic? How do you design a function? How do you design the activity that the player is going to undertake, right? In such a way that they'll be interested in continuing to undertake that thing to do it over and over and over again. And that's hard enough when you aren't also including the educational aspect.
00:14:48
Speaker
It's hard enough and you aren't also trying to teach them something about the world or something about, you know, anything. So it's definitely something that we're exploring and it's definitely a fundamental concern when it comes to making games, let alone educational games. So, you know, we're trying our best and hopefully we've made something that can at least start a conversation about how we can try to do that better going forward.
00:15:12
Speaker
Okay. Well, I think with that, we will take our first break. And on the other side, we're going to talk about the game you guys have developed again called Algorhythm. And it's all about data science. And we'll get into that. In the meantime, we've got some ads coming up. If you want to help us out for the archaeology podcast network, be sure to head over to arkpodnet.com slash members. And you can help us out for less than a Netflix subscription a month and about the same as an expensive coffee at Starbucks.
00:15:42
Speaker
So that's not too bad, and you keep this kind of education out in the space. Back in a minute. Looking to expand your knowledge of x-rays and imaging in the archaeology field? Then check out an introduction to paleoradiography, a short online course offering professional training for archaeologists and affiliated disciplines. Created by archaeologist, radiographer, and lecturer James Elliott, the content of this course is based upon his research and teaching experience in higher education.
00:16:07
Speaker
It is approved by the Register of Professional Archaeologists and the Chartered Institute for Archaeologists as four hours of training. So don't miss out on this exciting opportunity for professional and personal development. For more information on pricing and core structure, visit paleoimaging.com. That's P-A-L-E-O, imaging.com, and check out the link in the show notes.
00:16:27
Speaker
Welcome back to episode one 81 of the archaeo tech podcast. And we are talking to JD Calavelli about the gamification of education. Now we've kind of hinted around
Algorithm: A Data Science Game
00:16:38
Speaker
this. So let's talk about the game that you guys have developed. Algo rhythm, which I love the name, by the way. I just kind of hope that that's somebody's name or something like that. Algo rhythm. I don't know.
00:16:48
Speaker
It's a brainstorming session across our entire team. When somebody just sort of, a fellow analyst named, he was very good with puns. Like that's his thing. He was like, guys, I got it. Like, and we were like, what are you talking about? You don't have it. We just stepped into the room. And he's like, no, we totally got it. And he pulled out algorithm and we were like, okay, yep, that's it. We literally ended the meeting. When you know, you know. Yeah, when we knew, we knew.
00:17:15
Speaker
Nice. Nice. All right. So what is algorithm? What are you trying to teach? I sort of discussed a couple of things that I've worked on at risk up until this point, but one of our biggest initiatives and one of the initiatives that I think we're most known for thus far is our Data Science for Everyone initiative. And so we started a coalition with a bunch of different stakeholders, quite a number of different stakeholders around the country that are interested in advancing the teaching of data science in K through 12 education back to sort of like Steve's
00:17:45
Speaker
you know modus operandi of like data is important we need to be reading data we need to be data literally need to be using data to find interesting information interesting elements of the world that perhaps are hidden from our immediate view and starting that conversation about how to talk about data and how to think about data much earlier than where we currently are.
00:18:04
Speaker
I know for me, it wasn't really until my junior year of college that I even had a course that was talking about what data is and how to analyze it. And the impetus behind starting Data Science for Everyone for Risk was to sort of make that first conversation happen earlier. And so for us, the reason why we created Algorithm was to try to start that conversation as early as we felt we possibly could. So we created this game for late
00:18:32
Speaker
like elementary school to early middle school students to sort of introduce them to some very, very fundamental, but very, very important topics about data. Cause we have this interesting conversation with some students when we were sort of, you know, when we were first testing the game, as we were developing it, you know, data-driven designing the game as we were developing it out. And we would ask students, you know, what, when you think of data, what do you think of?
00:18:58
Speaker
And a sizable number of students in that age group said the thing that your phone gives you to allow you to connect to the internet. Oh boy. And you would sort of like collectively facepalm, you know, and be like, oh my God. But also we totally understand, right? That's, that is sort of like culturally or rather like the zeitgeist understanding of, of what data is or the way that we sort of discuss data most prevalently or the word data comes up most prevalently rather like in, in regular contexts.
00:19:28
Speaker
So we wanted to start the conversation as early as possible. What is data? What can you use data for? Why is it important to understand data ultimately because you can use it to help you make more informed, better decisions? So that was kind of like our impetus behind creating algorithm and our initial sort of guiding principles.
00:19:49
Speaker
Yeah, that's actually interesting because that use of the word data, I mean, we use it so many ways, I would never have guessed the kids would have used it in that particular way. But of course, that's where they year in the house, I'm sure most frequently dad's and used up all our data this month. I know I said that to my kids a million times.
00:20:07
Speaker
Wow. Okay. There we go. Right back to looking at the world from multiple angles. I hadn't even thought of the basic meaning of that word. So anyhow, can you give us a little description for us, how the game works? I mean, I downloaded it onto my iPad earlier so I could play it a little bit, but can you tell us what the metaphor is, what's going on, and what specifically you're trying to teach about data through that app?
00:20:31
Speaker
Yeah, so happy to do so. So the game takes place on the dance floor and the player is tasked with acting as the DJ for this particular dance floor. And as the DJ, their role is to, you know, get the dance floor moving.
00:20:48
Speaker
And so the way in which they are tasked with getting the dance floor moving is by fulfilling certain requests or requirements with regards to the music that the songs that they choose to be a part of their playlist. And so students in the first two levels of the game are tasked with creating a playlist that fulfills a certain criteria. So, you know, make a danceable playlist, right? Make an energetic playlist, make it very
00:21:14
Speaker
tempo-driven playlist, right? And in the abstract, that's sort of like, okay, well, that's interesting, right? How exactly do I do that? And the catch is that we present students not only with the sounds themselves, the song themselves, or bytes of the songs themselves, but we also present them with some information about the songs.
00:21:32
Speaker
that has been derived specifically from the Spotify API. So Spotify determines like, oh, this song in our database has this number danceability, has this number tempo, has this number energy. And so sort of we were nudging the students, you know, ideally to utilize both their, you know, sonic intuition of like, oh, I heard this song, this song makes me dance.
00:21:56
Speaker
But not only that, you know, this song has a certain danceability score, danceability value that's been determined as a piece of data connected to this particular song. Right. And if I use those two things together, my own intuition about, you know, what is a danceable song and these, this information, this actual data about the danceability level of the song or energy or tempo, then I could create the best playlist possible. So that's the first two levels. The last level is just.
00:22:23
Speaker
taking requests from the dance floor up until basically you fail a certain number of times and you go for a high score. So, and the taking of requests works in a very similar way where it's like, Oh, this person has asked me to play a song that has a certain level of danceability and also certain level of tempo.
00:22:39
Speaker
or a certain tempo and a certain level of energy. And the catch is that you have a time limit, right? You can't spin through every single possible record to figure out which song should be used in that particular instance. You have to start using the data to help you make that decision in a timely manner.
00:22:55
Speaker
So again, we hope that we can frame this conversation about data isn't just the thing that powers your phone on the internet, it's information about certain things that can be utilized in conjunction with your own thoughts and feelings about a particular thing to make better decisions about that thing. In this case,
00:23:14
Speaker
what makes the best Spotify playlist, what makes the best song playlist, what will get the people dancing as much as possible, right? But that could be extrapolated further in any other number of ways, right? And that's the idea. Hopefully we're planting that seed such that it can continue to grow going forward. I hope that makes sense. Does that answer the question?
00:23:31
Speaker
It does. Yeah, I think it does. I just want to clarify my then understanding of it is what you're shooting for. You've got the tempo, energy, and danceability scores for each of the tracks. What you're trying to do is maximize a certain outcome based off of those scores. Exactly.
00:23:51
Speaker
cool. That's really cool. And I love this type of game. Anything that involves, I feel like music and rhythm and things like that in order and using that to kind of teach you other stuff. It's just one of those things that kind of sticks in our brain, doesn't it? So how long did it take you guys to develop this game? Cause I was actually, when we were starting to get around with this, I thought we were going to talk about some kind of a concept and then, you know, it's on the app store and you can download it and play it right now.
00:24:15
Speaker
And it looks like you created a whole company around either this game or apps in general. I'm not really sure. But you created a company around doing that. It's a free game. So how long did it take you guys to fully develop this once
Development and Collaboration on Algorithm
00:24:30
Speaker
you had the idea?
00:24:30
Speaker
Yeah. So the idea was sort of in the works and the game was in the works prior to me joining risk earlier this past year. So I would say all in all, it probably took us about two years and change. And actually we didn't make a company around the game. We had the benefit of working with a company out of Toronto called Enable Education that offered their own services as a development firm for us pro bono, because they believed sort of in our vision of what they should look like. And.
00:24:59
Speaker
Yeah, for that we're, we're, you know, incredibly, incredibly grateful for them and we'll continue to sing their praises for the amount of hard work that they put into this project as well. Yeah. You know, the game was, it was a long time in the making and a lot of that was, you know, development time, but also a lot of it was, you know, living by our own award where it was kind of.
00:25:17
Speaker
us actually testing and making sure and gathering data about our game and the extent to which it actually does do what we hope it does, whether it actually teaches students these fundamental things about data and whether it helps them kind of better understand what data actually is. So living by our own practices was a lot of that as well.
00:25:36
Speaker
Yeah, one thing I think is interesting, and Apple's been doing this for a little while now, it tells you when you go to the app store what data is collected against you. Usually that means private data, like the data that's personally about you. It's interesting for a game about data science that no data is actually collected, is what it says on Apple. But I'm wondering, that means no personal data, so I understand.
00:25:59
Speaker
We don't collect any personal data like that, but we do collect a little bit of like, you know, average session time, you know, where's your drop off, that kind of stuff. Which is interesting that that doesn't show up in the app store, but also, yeah, no, we don't need your name. We just hope that you're learning something.
00:26:16
Speaker
Yeah. And that's what I was actually getting at is what other kind of, like you mentioned, session info and stuff like that, but what else are you guys collecting in order to, I guess, learn more about how the app is actually helping people learn more of our helping children learn more about data science? And then are you making continuous improvements to the app based on that? Or is it just kind of like it's living out there now and you'll keep it alive, but you're onto something else?
00:26:42
Speaker
Yeah. So, you know, we kind of view it from two different angles of like the more quantitative stuff of like, what are the monthly active users? What are the daily active users? You know, how long is the playtime? What levels do people get to? You know, do they get to the very end? You know, how long do they play at the end? And a lot of that is more like, Oh, you know, are people playing our game? Right. Is it, is it fun enough that it keeps people involved?
00:27:05
Speaker
And again, that's more quantitative and that's more to the kind of gamification, the initial point about gamification of like, oh, hopefully you can serve as an incentive structure to get people to engage with things that they otherwise might not necessarily engage in. Right. And in this case, that's, you know, learning a little something about data science and students learning a little something about data science.
00:27:23
Speaker
The qualitative side is probably more of where we get our fill of have students actually learn this or have they actually gotten something out of it. And that has come more specifically from conversations with students, conversations with teachers that have use in the classroom or planning on using it in the classroom.
00:27:41
Speaker
and have made the case to us that it's sort of like a helpful thing for them. And I think people oftentimes kind of forget that data is more than just numbers and what you can see on an Excel spreadsheet. It's also that qualitative element of like, we got this information from these teachers specifically that said that it's helpful.
00:27:58
Speaker
so you know it's the combination of both of those two things that's helping us kind of drive the direction of it going forward and ideally you know we're not entirely sure to be honest where where we'd like to go from here we'd love to see you know the game continue to be played and we'd love to get you know people's reactions and thoughts about how it can be improved going forward and
00:28:18
Speaker
Hopefully, we can continue to improve it a little bit or maybe take what we've learned and apply it to something else and something different. But yeah, we just released, so we're hoping that students and people can get a little bit of something out of it and then we can get some information from them about how we can hopefully make it better, do something better in the future. We're still early on, so we're hoping to keep going, keep on keeping on. Indeed.
00:28:44
Speaker
You hinted at it a bit there and it was a question that I had actually written up on our notes here. Do you actually have a lesson plan that goes along with algorithm? Is the intent like solo exploration child will download this and play the game and learn or is it intended for some kind of guided, especially like a classroom environment? Is there an overarching approach that you're taking not just for this game, but also for any educational products that risk is producing?
00:29:12
Speaker
One of the big things that we always say at risk, and it's not a cop out, I swear, is that we're generalists, but we're not specifically, we know a little bit of everything, but we don't know anything all that well. And so part of how that expressed itself in this project is that we would like to work with teachers who have played our game and who find it interesting to develop
Importance of Teaching Data Science Early
00:29:37
Speaker
a more comprehensive lesson plan about how it can be actually utilized.
00:29:41
Speaker
One of the things that when we initially created it, one of the pillars of our design was that we'd want it to be something that a student could play, you know, and derive something from even without the existence of a lesson plan around it, even without sort of, you know, a teacher guiding through it. But we also hope that it could be something that could be utilized in a classroom that could be used, you know, by a teacher to help sort of, you know, teach, teach a little bit of something interesting on a Friday when they have some extra time or something like that.
00:30:09
Speaker
sort of in service to this bigger goal of us encouraging data science and data literacy in K through 12 classrooms. So hopefully one of our next steps, and we would love any sort of advice from anyone on this, is exactly how we could have that happen in a classroom and how that lesson plan can be created to have it work as well as possible in a classroom. One of our hope is that it sort of teaches, again, those three very fundamental ideas of
00:30:35
Speaker
There's data everywhere. There's data behind things that you may not necessarily think there is data behind, I guess for data isn't the thing that powers your phone and also that it can be used to help you make some decisions. But a more comprehensive lesson plan around it is hopefully in the works and more than hopefully in the works, we would like for it to be in the works with the guidance from more people, more intelligent than us in that space.
00:30:59
Speaker
Awesome. I've got a follow-up question about this, but why don't we take the break right now because my mind is buzzing right now with ideas that you're triggering.
00:31:30
Speaker
Yeah, very important question that we get very often. Unsurprisingly, it comes down to a couple of things for us at risk. Number one is that the concept of data, data privacy, data security, data presence, data as an object for exploration and for analysis is super culturally salient. And not only is it culturally salient, but it's just like
00:31:37
Speaker
All right, we'll be back in a minute.
00:31:56
Speaker
fundamentally important when it comes to navigating the world, in our opinion. There's a certain level of world literacy and information literacy that you can get when you can analyze a dataset, or even more broadly, when you can look at a graph
00:32:12
Speaker
correctly, right? Because data science sort of expands far beyond the idea of like looking at an Excel spreadsheet, right? But it's just the whole concepts behind like looking at representations of data, creating representations of data, having those things say something, having them represent something, you know, being able to parse out exactly what they're trying to say and exactly what they're representing. You know, this is just like, not only is it culturally relevant, but it's just the direction that sort of all a lot of inquiry seems to be going now.
00:32:40
Speaker
And in our opinion, it's better to have that conversation start as soon as possible as opposed to sort of push it off down the pipe until you might take a class on it in a political science, sociology course or college. The thing is, it's important enough
00:32:59
Speaker
elements of our daily lives now that we don't feel like we can afford to push that off on students anymore. It's something that all students should have a certain baseline knowledge of prior to entering the workforce, prior to entering the world, because it's just so fundamental to being able to learn and grow and understand and take in information as we've gone forward in time.
00:33:25
Speaker
Yeah, it makes sense. Certainly, as different kinds of data are more accessible to more people through more different venues, being able to assess them and interpret them is useful. A follow-up to that question then is, is there a sweet spot in terms of age for kids to start learning about data? You're targeting late elementary, early middle school, which incidentally is what I've worked with when I was in ed tech primarily.
00:33:50
Speaker
Have you gotten into the data about when it's best to teach them about data? Yeah, so we haven't done anything specific on that front, but from our own anecdotal experience. Data science as an idea or data science as a discipline, like most other disciplines, build off of themselves.
00:34:09
Speaker
There are certain elements that you can learn earlier on that can help inform and set you up to be able to learn other things later on. One of the things, when we were talking to Steve about this project initially, he was like, let's make a game to try to teach fourth or sixth graders how to do a regression analysis. I was like, Steve, I don't know how to do a regression analysis.
00:34:32
Speaker
I was like, let me go learn how to do a regression analysis. And then I'll tell you if I think we can teach fourth through sixth graders how to learn to do a regression analysis. And that's our facetious way of saying, or jocular way of saying that there are levels to this. There are certain levels of expectations of what you should know at different stages.
00:34:56
Speaker
And we hope that, you know, even in that age range, right, we, we're at the point now of combating or rather just filling any gaps in terms of like lack of information of what data data is and what it can be used for. And so we hope sort of in the younger age groups that we can just start those fundamental conversations of like, here's data, you know, what is it? What can it be used for? Right. And then maybe some rudimentary visualization stuff where it's like, you know, a graph.
00:35:24
Speaker
is really more than just numbers on the line, right? It's demonstrating something that can be used to demonstrate something. And then, you know, as you go on in your career, then you can start entering more of the statistics adjacent stuff, you know, maybe in high school, definitely in college of like, you know, how can we prove that certain things are true? Or how can we get as close to proof about certain things are true? Because the one thing you'll notice here is correlation is not equal causation. I might get that tattooed at this point. I've heard it so many times.
00:35:51
Speaker
But the point being, you graduate from an earlier space of like, oh, what is data and why is it important? And then how can I use it to start making decisions? And then it even pipes into computer science and machine learning. A lot of that is fundamentally data science. And so if you ultimately get to that point and you're still this interested in data and how we can use it to make better predictions about things that you can start learning about,
00:36:20
Speaker
you know, k-means clustering and neural nets and so on and so forth. So, you know, it's just, again, to the earlier point of like, it's this space that sort of exists across many different disciplines that we feel is yet sort of underexplored in the K through 12 space. And, you know,
00:36:38
Speaker
The, again, the sort of like joking way that we described is like, how many times have you ever used a TI-84 calculator after, you know, a high school pre-calculus, right? Or high school calculus, right? But how many times in life do you need to be able to analyze a set of information and be able to make, you know, educated conclusions about it? And so that hopefully, you know, can build up further and further and continue to grow as, you know, with a student as their interest in the area grows.
00:37:07
Speaker
If they want to take it all the way to machine learning, great. If they want to know how to, you know, make macros in an Excel spreadsheet, great. If they'd like to learn how to visualize in R, you know, make data visualizations in R, that's great, but it can grow and it can change. And there's so many avenues that you can go towards. And it's just a question of like getting that exposure early on because it's the exposure that seems to be missing right now.
00:37:28
Speaker
Yeah, it totally seems like looking at especially say kids that are in fourth to sixth grade right now, you know, just because of the advancement of technology, most of your manufacturing and other type jobs that don't really require much beyond a high school education are slowly going away, right?
00:37:46
Speaker
we're either automating those, making them more efficient, or actually developing robots to do those jobs because it's cheaper. So as that happens, people are going to have to be better critical thinkers and actually use this kind of thing. Because like you just said, I mean, I'm 47 and I heard that growing up, like, Oh, you'll never use this math outside of high school or something like that. And that's like,
00:38:06
Speaker
kind of sad. Why shouldn't I use this math outside of high school? It's because most of the people I grew up around are in construction or doing something where they literally don't use that kind of thing on a daily basis. But I think some of the jobs that may be out there in the future, you will actually need to think this way. Even though a computer might be doing it for you, it's helpful to understand the reasoning and the why in order to actually tell the computer and what to do in the right way, and then understand the results that you're getting back.
00:38:33
Speaker
One of the ways that I, to that point, that I kind of like conceptualize this is like, at one point in time, there was a brick layer, right? Like the job was to go lay bricks somewhere. My great-grandfather, or rather my grandfather, sorry, immigrated to the States from Italy. And he kind of worked in that space. He did construction. And there was a time when, you know, he was a big buff.
00:38:58
Speaker
man, carrying bricks around. That was a construction job. The reality is that that kind of job has disappeared because we created bricklaying technology. We created bricklaying machines. But in the absence of the bricklayer job, there has created a necessity for the bricklaying machine manager in this weird
00:39:23
Speaker
The death of one created the birth of another. And what is the skill set that you need to manage a set of machines, like data science, to a certain extent? You need to be aware of, OK, I have 50 machines. All of them are creating or laying this many bricks in this amount of time. When we put that all in the spreadsheet, I want to try to maximize the amount of time. Well, maybe let me A-B test this. Maybe if I have 10 machines running,
00:39:50
Speaker
you know, in one place, then overall, as opposed to five and five, then overall, my amount of time, you know, to lay the entire bricks for the house will increase. You know what I mean? Like, I'm, I'm here, right?
Future Job Landscape and Data Literacy
00:40:01
Speaker
But the point, the point more specifically is like, you know, to be literate in that space, you need a certain exposure to like.
00:40:09
Speaker
what is data and why is it important, you know what I mean? Like not to beat the dead horse on it, but it's like, yeah, it's, it's, it's interesting because I think a lot of people have sort of recognized this problem of like, oh, well, there's a lot of jobs that seem to be disappearing. And we seem to not be preparing people in high school and beyond to like do the new things that are coming. Right.
00:40:31
Speaker
But the missing piece is like, Oh, well, one of the things that we seem to need to understand or need to teach students to understand is this element of like managing data, right? And understanding data and being able to make decisions about it. Right. So, you know, back to, back to the initial point of like, that's why we think it's important to be teaching all students at every age, what these, what this is and what this, what this means and how it can be utilized because, you know, we.
00:40:55
Speaker
We believe that it's important in every function, right? We had the benefit of going out and interviewing Sal Khan of Khan Academy because Khan Lab School sort of piloted an initial program for data science in high school, in a high school setting.
00:41:12
Speaker
And one of the things that he said that I think is really prescient, and I'm not quoting him directly by any means, but one of the things that he said that I thought was, again, very prescient was like, 50 years ago, who had big data, maybe major companies had big data, insurance companies maybe had big data, a lot of data from which you could derive decisions. The reality is now, everyone has access to that.
00:41:40
Speaker
Not only that, but there's a certain expectation of the ability to utilize that to make better decisions about whatever it is that you're doing. Whether it's just searching Google or finding a data set on Kaggle or pulling from an API, an openly hosted or privately hosted API. The access is there in a way that it's never been before. And the reality is when the access is there, there's a certain expectation of ability to utilize it.
00:42:07
Speaker
Yeah. So you're making a strong case for why people have to be not just numerate, but comfortable with analyzing and applying data to their lives in various ways. When it comes to teaching kids, I'm getting kind of a vibe of the same reason why you would teach them a foreign language when they're fairly young, so that it becomes a natural part of how they think and how they can interact with the world.
00:42:35
Speaker
So you settled on gamification and there are many, many different ways to teach people, gamification being just one of them.
Gamification's Role in Education
00:42:43
Speaker
So could you just explain why you want that particular route and maybe touch on what you think the strengths and maybe even some of the limitations or weaknesses of gamification are as an educational tool?
00:42:54
Speaker
Yeah, for sure. We did decide to approach the gamification angle. From a top-down perspective, what's interesting about gamification is it challenges the idea of what work is. We have this cultural conception that work needs to be not fun.
00:43:15
Speaker
like the absence of fun equals work, right? And, you know, we- All work and no play. Yeah, all work and no play, exactly. Or like work hard so that you can play hard, right? And, you know, obviously there is importance to recognizing work as a discipline that requires effort and, you know, and focus and attention and so on and so forth. But to like completely divorce it from the concept of play, right, or from the concept of learning through play,
00:43:45
Speaker
kind of doesn't sit right with me. And I think it also doesn't sit right with a lot of individuals at risk as well. And while we're not developmental psychologists, again, generalists, no knowledge of anything. Socrates, all that I know is that I know nothing. But the one we're not developmental psychologists is what it means.
00:44:04
Speaker
When you're a kid, I remember, or my parents told me that when I was a kid, right, there's this huge emphasis on like learning through play, like learning through exploration, right? Like how your kid put this, you know, square peg in the round hole and be like, oh, that doesn't work. Like, why doesn't that work? And then try something different, right? But then after, you know, you're no longer a kid, like a kid-kid, that seems to all disappear.
00:44:26
Speaker
you know like exactly like puppies play fighting right like there's a space where it seems to be good to allow for a certain level of play and actually through that play you learn something right but after you're like a baby you know that that sort of understanding of play and and it's and it's respect or how it works in in respects with work right or rather how it can sort of be utilized to help facilitate work like disappears right or seems to disappear
00:44:53
Speaker
And so again, from like a higher level perspective, we feel like game vacation is really interesting because it kind of merges that space again, where it's like, oh, play is good and we can learn from it. And work doesn't necessarily need to be no fun, right? And in fact, like maybe through some fun, you can actually learn more than you would have been able to learn if you didn't have any fun, like you were doing it, right?
00:45:14
Speaker
Again, anecdotally, I remember what put me back on this path a very, very long time ago. Now, not that long ago. But what put me back on this path of gamification and being interested in this space was a game that I played in sixth grade that was a simulation of the Black Death.
00:45:33
Speaker
and how it affected medieval Europe. And it was a game with dice and rolling and you had to make rules and you had to defend your town and people could die because of the Black Death if you didn't quarantine correctly and so on and so forth. And I'll go out on the limb and say, I don't remember that much about sixth grade.
00:45:52
Speaker
at all, but I do remember that. And I, to this day, remember that. And remember that experience as being super meaningful to me. So, you know, again, this is all sort of like higher level, but we feel like, and I personally feel like games has the, games have the potential to really meaningfully get people to engage with something.
00:46:12
Speaker
Right? Mm-hmm. You're talking about sixth grade, and this is, again, another one of my asides because I got old brain. When I first started working at Dalton, it was because of this game that was produced at the school based off of an archaeological excavation, and it was a collaborative
00:46:29
Speaker
group project and we've continued it on. Actually, I've been helping them redo it for the fifth grade now. So we're talking about roughly the same grade range that you're looking at with algorithm and using a game metaphor, some of the game incentives in order to teach kids.
00:46:49
Speaker
I also have, despite having been doing this for a long, long time and understanding the value of it, I also have a complicated relationship with gaming and gamification as an educational tool that we don't have to go in now because it's totally an aside. I think about different kinds of gamification. For me, for example, Duolingo, which is a very famous way of gamifying language learning, is actually really effective for me to brush up on languages.
00:47:19
Speaker
I get very angry when I do it. I get very frustrated. They keep giving me all these nudges and trying to keep me in its environment. It's extremely helpful anytime before I travel overseas. Even though they have these layers of
00:47:37
Speaker
social interaction where you can compete against other people or post your progress as you go. I don't want to do any of that. I just want to get it to quit bugging me. But that's a very solo activity and that stands in distinction from that archeotypes of that excavation.
00:47:55
Speaker
which was an organized group activity with a different sort of incentive structure and not an in-the-game incentive structure. So I don't even know that I've got a question here other than a comment. There are a lot of different kinds of incentive structures that can be built around gamification. And maybe I guess if I do have a question, it's how did you decide on the particular incentive structure that you built into algorithm that allows the player to advance?
00:48:23
Speaker
Yeah, I mean, Duolingo is a very classic example of, like, gamification done right, or what a lot of people perceive to be gamification done right. But in my opinion, Duolingo is, like, gamification
00:48:37
Speaker
done the way that people expect, which is not necessarily a bad thing, right? But it is sort of like the expectation of like, you know, how do you make something into a game? Oh, well, you make it a competition, you give points and that's a game, right? And I think, you know, for, for in a lot of cases, yeah, like that, that can be a game, right? And, you know, no, no knock on Duolingo at all. I think it, you know, works very well. Definitely. It seems foreign language acquisition.
00:49:03
Speaker
But I think, and back to something I mentioned much earlier, gamification and how to make a game so that it teaches people things is still very much in its infancy of how do you do that? Because when you ask people, when you ask a lot of people, at least anecdotally in my experience, when you ask people, oh, what is gamification? Or what do we want gamification? Or how would you expect gamification to work? They're like, oh.
00:49:31
Speaker
put points on it and make it a competition. And that's how you gameify something. And so we, with algorithm, really wanted to try to challenge that a little bit. And so our idea then was to develop an incentive structure where the only way that you could meaningfully engage with the mechanic of the game is by understanding
00:49:52
Speaker
the information, the subject matter that we expect you to understand, that we hope that you can learn by virtue of playing the game. So it's a little bit backwards, but the way that I would explain, the simplest way I think I can explain that is like in the last stage of the game, you know, the mechanic of the game is dragging and dropping songs onto requests so that you can fulfill them to get a high score.
00:50:15
Speaker
But the conflict is that you have a time limit. You don't have all the time in the world to be able to make this decision. And so when you have a list of 100 songs to go through and only 30 seconds to make a choice about what fulfills this role or what fulfills this request,
00:50:35
Speaker
right? You have to use the data in order to progress in the game, right? In order to get that point, you have to understand that, oh, I need data because with using data, I can make this decision faster and more accurately than I could have if I didn't use the data, right? And so it's not quite putting points on things and making the competition. It's a little different. It's saying,
00:51:01
Speaker
No, in order to advance, you need to fundamentally understand the thing that we're doing because if you don't, then you'll fail the game, right? Not sure if that makes sense, but I think that's kind of where we were going with it to answer that question.
00:51:16
Speaker
No, thank you. That makes sense. Yeah. So Paul and I have a lot more questions to ask JD and he's willing to stick around for a bonus segment. So if you're a member of the archeology podcast network, head over to arc pod net.com and check out the bonus segment area in your members area. So if you just go to the top of the page or click on the hamburger icon, you can see your members area and then go to bonus and, or actually just go to HQ downloads or free downloads, whatever we call it. I don't even know. I made the whole thing.
00:51:43
Speaker
But anyway, go there and you'll find the ad-free version of the episode and then the bonus segment sitting right next to it. And it's right there. So if you're not a member, head over to arcpodnet.com forward slash members, support us, get more stuff like the bonus segments and other things. And with that, I think we will say goodbye to JD for this part. But again, members, check out the bonus segment that we're going to record to continue having this conversation with JD. All right. We'll see you guys next time. Thanks, everyone. Thank you.
00:52:16
Speaker
Thanks for listening to the Archaeotech Podcast. Links to items mentioned on the show are in the show notes at www.archpodnet.com slash archaeotech. Contact us at chris at archaeologypodcastnetwork.com and paul at lugall.com. Support the show by becoming a member at archpodnet.com slash members. The music is a song called Off Road and is licensed free from Apple. Thanks for listening.
00:52:42
Speaker
This episode was produced by Chris Webster from his RV traveling the United States, Tristan Boyle in Scotland, DigTech LLC, Culturo Media, and the Archaeology Podcast Network, and was edited by Chris Webster. This has been a presentation of the Archaeology Podcast Network. Visit us on the web for show notes and other podcasts at www.archapodnet.com. Contact us at chris at archaeologypodcastnetwork.com.