 This session, we are happy to introduce a terrific session called Blue Skies on Mars. Our speakers are, well, Cynthia Colloyne, Andrew Stricker, and Barbara Truman. Cynthia, that would be me, Lerlobo, is a professor at Colorado Technical University and at the Colorado Community College System. Andrew is an instructional architect at the Yale Air University at Maxwell Air Force Base in Alabama. Barbara is a graduate faculty member and researcher at the University of Central Florida in Orlando. Welcome all. Let's begin the session. And I created a speak easy and, of course, forgot to hit any of those little things, but the good news is I'm also presenting, right? So, today, I'm happy to introduce Andrew Stricker and I'm going to turn the floor over to him. And I'm going to pepper the chat log with a few of the comments about the slides as we get started here. So, Andy, over to you. Okay, thank you, Leroy. I'm very happy to be able to come and share. We've been talking about the simulation of various venues for the past several weeks, you know, with all the excitement that NASA and others have resulted in in the InSight Lander on Mars, so it's very timely. We have worked with the original simulation with NASA JPL and we've extended the work into OpenSim and we've been having a wonderful time building this newer generation of the simulation and we're going to talk to you about it a little bit today. But part of, you can go to the next slide, Cynthia, part of the fun with this whole effort is, you know, we decided to meld our science fiction interest. We're all avid readers of science fiction and we wanted to really create a simulation that brought the science and the fiction together in creative ways. And so, we took this quote out of the original script of Blade Runner and to kind of represent the fun that we're having, you know, so much of what we've done. We try to create a very compelling experience for participants to the simulation to get a sense of the thrill and the excitement of traveling to Mars and at the same time we want to make sure that we're bringing in the science about human space travel. The original simulation was based on a White House study that was championed by NASA to look at all the various options for human spaceflight into the future. And there's quite a few challenges, as many of you know, and we introduced those challenges in the simulation. Next slide, please. So what we've done is we've taken the work of Gary Klein, he's highlighted this really compelling nature of problem solving where he says with Ann-Marie Slaughter that, you know, there is a common type of problem that we deal with. They can be very complicated problems and we basically, you know, work on those problems pretty much knowing that, you know, most of the pieces of information required to get to fairly good solutions, but they can be, you know, quite involved. And for example, if someone gave me a Swiss watch and, you know, God forbid I dropped it and it busted into a thousand pieces. It may take me several years, but I can probably put it back together again. But when you look at a complex problem, you know, let's say that Swiss watch fell on the floor and someone, my back was turned, came down into the room and took several pieces away, but I wouldn't know it. I wouldn't know whether I had all the pieces or not. Well, that changes the nature of the problem from complicated to complex. You know, when we really don't know for sure if all that can be known is knowable. And so as it turns out, a lot of problems in life are complex. And so we wanted our students to be able to discern, you know, what is it like to work on a complicated problem and how to recognize when you're actually dealing with a complex problem. So this simulation takes you through the different mindsets associated with those very distinctive types of problems. Next slide, please. So this process that we use is not new to many of you that are listening to us today. It's a fairly common process of rapid prototyping, but the one thing I do want to highlight and I'd like to ask Barbara and Cynthia to share their thoughts about this too, but we have a French village that we created. We're both, I think we're all excited about the French culture and even our French is a bit rusty. We do enjoy coming together each Sunday. We've been coming together each Sunday afternoon for several years where we basically brainstorm in this wonderful salon area. And so we invite our colleagues and I think some of you in the audience have been out there too and visited with us. And from this brainstorming environment, we basically started a creative process to build devices that would support our learning objectives for the simulation. So as you go through and you look at this process, we've been busy putting together a federated architecture where we can plug and play different types of virtual devices. We've taken the work of Nancy Nasarian who had been at Georgia Tech, now she's at Harvard, but we've taken her work in model-based reasoning and we've applied it to our virtual devices. And we absolutely love the kind of environment where people can go in and do things with the instruments and manipulate what they do and see the second and third order effects of their decisions. And this is what Nancy's work has been about. And her work has been applied in medical communities quite extensively and we're applying it in our educational simulations. And we've also have worked very hard to build dashboards that present interfaces for the administrators of the simulation as well as the user data and also feed it back into the game environment so people can see how well they're doing. And so we put all the parts together in this architecture. It's a cloud-based architecture and so we'll have more to share about that here in a little bit. Now, thank you to Sin for moving me along. The game flow is represented here and I just want to highlight that as you go through the simulation, we've introduced this fun interface and we call it the Red Queen Interface and it comes through the looking glass of Lewis Carroll's novel and where she encounters the Red Queen Alice does and she's encountering a world where it's hard to figure it out much like a complex problem. You don't know if you should turn right or left, go fast, go slow and what's really interesting about Alice's journey with the Red Queen in the story is that she learns that a lot of things are counterintuitive when it comes to dealing with things that are high levels of uncertainty and very ambiguous. And indeed, this is very much the case with complex problem-solving. Barbara Ascend, you've got comments here, okay, next slide. So in the simulation, we have seven gameplay levels and so as you work your way through the simulation, the challenge gets addressed so you get additional clues and you figure out basically all the things that the environment can offer you at each of these levels. So you start out at a launch facility and you get on a rocket and you fly up to this space station and from there you start interacting with the Red Queen systems and you start getting information from the team that's on Mars and they're facing all kinds of challenges and you're basically going to Mars to help. And so as you work your way through these levels, you get pretty involved with dealing with a lot of types of information you have to make sense out of and put it together and you respond back to the Red Queen with open-ended responses. And so we've gone through several iterations of the models and the devices that are each of these levels. Barbara Ascend, you want to talk about some of your thoughts with these levels? I'll jump in real quickly and it's just so exciting to be able to integrate some of the existing science that as we look toward how to do this on Mars or in microgravity, it really helps us think about how we can solve this on our planet. Okay, great. Next slide. I'll jump in on this one. We're doing a lot of different things. So first off, it's important to realize all this is running on OpenSim, the latest version. Andy hosts this from a farmhouse and of course we've extended the memory since past years where we presented at OpenSimulator and we have this learning analytics dashboard and we also have this ontology that we're basing on, I think, MIT's ontology for the semantic web. But we're thinking through how this linked data is all classified, how people respond to these various stimuli and then collecting this information on a later slide, you'll see how it goes from challenge to ontology to certain outcomes and certain reflections. So we're really trying to take a very deep look. We're not just going through and playing a game and falling out the other end. It's not about the wind condition. It's really about making good decisions, but with missing pieces of information, with a high degree of uncertainty, with the inability to breathe, things like that. And so if you ever come out and see we're all wearing spacesuits, we have a health meter on, we start feeling less healthy and one of the things I injected in the game is I wanted some first aid. I'm so used to playing games in which you recover or where death is not forever, right? So I needed strategies that would help you to recover from mistakes because to me, learning is about failure. If we already knew everything before we entered a class or a game or whatever, there would be no reason to take the class or to play. But so we need a challenge and we need some failure and we need some recovery or discovery from that failure. So that's what this is about. Over to you, Andy. Oh, that's great. And I'm so happy that you brought up those aspects that people can actually try things out and see if they work or not. And so what we wanted to do was take their way of describing how they're making sense of things. And so this ontological structure that we got from MIT, we created a way for it to be augmented and add it to from other ontologies. And so if you go to the next slide, we've got a snapshot from an output that a player in the simulation would see. So when you're responding to a complex challenge by the Red Queen during gameplay, you'll get this type of feedback. And so what you'll see is that the AI engine will break out all the constructs in your open-ended response and it will map your constructs to topic areas that it retrieves through the ontology. And then it assigns a relevant score. And these relevant scores can be weighted by the instructors. And then it offers up these links for further exploration so it maps to the science related to the complex challenge that you're being introduced with. And so in this way, you sort of get a mechanism to help people understand that when you're dealing with complex problems, there's multiple dimensions that you have to pay attention to. And usually those dimensions are interdependent. And so you have to make sense out of these interdependencies. And so the engine actually gives that kind of feedback to help people understand all the different relationships that might be at play as you're looking at the complex challenge. And here, Andy, I'm about to move you forward. It'll take just a moment for everyone to hear. And thank you. This architecture is amazing. And the reason it is is it's so extensible, adaptable, and of course, offers opportunities for others to collaborate. Go ahead, Andy. Well, thank you. The, as I mentioned before, it's a federated design. So there's databases are shared by our Unity interfaces and our OpenSAM and our Echo device. And so as you see in the illustration, we've got these consoles in which you interact. And there's also part of a decryption tool sets where you get encrypted messages, you decrypt them. And then you feed them into the Red Queen system to help you interpret the clues. So clues are being fed to you through this Enigma-like device. And then you take that information and use it to craft your responses. And so although we have our prototyping environment on a Linux server off the cloud, we also have it running on the cloud. And so the data moves onto the Amazon cloud through our API gateway. And then we use Lambda to turn on certain parts of the code on demand. So we're kind of excited about this because Lambda gives us a way to only run cloud resources when they're needed. And then we're also excited because we can share our databases and our data flows across these different platforms. And just one more quick point as well that while these environments can be used for individuals to go through, also some of the levels involve teaming. So looking at how the different types of thinking styles that teams would use for particular challenges, this is all part of the exploration. Yes. And we are excited about creating these kinds of simulations as Barbara's highlighting where you can go through it by yourself and you can go through it with others. Because there's a big difference when you're problem solving as an individual versus when you're problem solving in collaborative team ways. And so that's, thank you, Barbara, for bringing that up. Of course, we've got to pay tribute to Robert Heinlein. So this is our time for groping. If you'd like to ask questions and or share some of your thoughts, just from the one I'm hearing about the presentations today, I see so many possibilities for the future with where OpenSim is going and the exciting work that's being done by the community here. So it's great to be able to connect with all of you. Thanks, everyone. Oh, and I guess as moderator, I better get back to the script. That's the only problem with having so many windows open. Thank you, Cynthia, Andy and Barbara, for a terrific presentation.