WEBVTT 00:00:00.000 --> 00:00:00.000 Okay, so you're welcome to introduce yourself in the chat window I see a lot of folks already saying hi so we're a community and I welcome that isn't that's wonderful. 00:00:00.000 --> 00:00:08.000 I know we're still waiting for a few folks to trickle in, but we just have some, some preamble that we can go over now in between so so I guess we'll get started. 00:00:08.000 --> 00:00:21.000 The first is that there is this is a notice that we are going to be recording this meeting and so we just wanted to let you know that in case you wanted for any reason to turn off video, or anything else. 00:00:21.000 --> 00:00:27.000 That is your cue Molly to start recording. 00:00:27.000 --> 00:00:31.000 Gotcha. 00:00:31.000 --> 00:00:40.000 And just a quick thank you to all of our. 00:00:40.000 --> 00:01:04.000 And just a quick word to say thank you to all our sponsors we work across multiple organizations like Edson, which is an NSF includes project cubes have an SF, I use Project Blue RCNUV project score RCN up, and I did bio number Molly, that's what I use 00:01:04.000 --> 00:01:10.000 her were DPI CPI RCN right. 00:01:10.000 --> 00:01:25.000 Anyway, model is like gonna stay muted but just just want to give you all a sense our community all a sense of, of all the folks that come around to the table to make this possible and all the folks that really have a genuine interest in inclusive teaching 00:01:25.000 --> 00:01:29.000 inclusive pedagogy and issues of inclusion and stem broadly. 00:01:29.000 --> 00:01:35.000 So thank you all for joining us here today. 00:01:35.000 --> 00:01:45.000 This webinar series has a few goals one was, is to initiate discussion on topics related to inclusive teaching practices. 00:01:45.000 --> 00:01:54.000 We started this early last year, about a year ago since like a year old. 00:01:54.000 --> 00:02:09.000 We wanted to build community among diversity of STEM disciplines. We wanted to raise awareness of the existing knowledge base, and existing resources that are out there to support instructors who are interested in inclusive practices. 00:02:09.000 --> 00:02:13.000 And we wanted to also support our educators. 00:02:13.000 --> 00:02:20.000 More recently, to think about inclusion in the time of Kobe 19. 00:02:20.000 --> 00:02:27.000 Just a few things I want to go over in terms of practices that we use for this webinar series if this is your first time joining us. 00:02:27.000 --> 00:02:46.000 Please add your name and pronouns, to your zoom name that you'd like to be called. If you have any questions that helps all mics will be muted at first, but you can certainly enable them during discussion and I believe this session will include breakout 00:02:46.000 --> 00:03:00.000 rooms for you can do that, private chat and file share or disable for participants that you can use chat to talk to everyone, please return to mute after you speak. 00:03:00.000 --> 00:03:14.000 And feel free to participate in whatever way works for you. Best, whether that be in question and answers by raising your hands and talking verbally or by using the chat window. 00:03:14.000 --> 00:03:33.000 As far as this particular talk what we will do is use the chat window. In order to invite questions that might come up in the course of the time we're able to do that because we have a whole collective set of presenters and so they can be monitoring while 00:03:33.000 --> 00:03:47.000 somebody else is talking. And then at the end we'll try to reserve some time for just general q amp a but as I said before, we'll have some discussions like breakout rooms and other things so plenty of ways to get engaged and just participate, the ways 00:03:47.000 --> 00:03:49.000 that you can. 00:03:49.000 --> 00:04:05.000 And if, if there are certain points where we are inviting discussion sometimes we pause to let folks tight, or figure out how to raise your hand. Don't be alarmed if there's ever some dead air around that. 00:04:05.000 --> 00:04:15.000 There might also be you know problems along the way with video or audio but please let us know in the chat if anything happens, Molly is monitoring the chat for any technical issues. 00:04:15.000 --> 00:04:19.000 we might happen. 00:04:19.000 --> 00:04:29.000 But if we will try to be prepared for all technological glitches the best we can, in community together with the flexibility. 00:04:29.000 --> 00:04:47.000 And we do have a survey that we send to all folks at the end of the talk so just want to give you a heads up please. When you exit out if you could take that survey, you can open it now so you don't forget on your browser, Molly will have the link to 00:04:47.000 --> 00:04:50.000 the chat. 00:04:50.000 --> 00:04:59.000 Okay, so since our first episode of 2021 we're really excited to bring a group of people here today. 00:04:59.000 --> 00:05:07.000 They will go a little bit more introduction themselves, but we have folks from University of Richmond Rollins Trinity Furman. 00:05:07.000 --> 00:05:15.000 And so, this is just a great opportunity to get to meet a lot of people. 00:05:15.000 --> 00:05:30.000 And we're going to be talking today about mathematical modeling data science covert 19, all of these really rich discussions that have invaded the classroom. 00:05:30.000 --> 00:05:35.000 So we have five speakers panelists. 00:05:35.000 --> 00:05:40.000 co collaborators that are going to be representing a project that they're working on together. 00:05:40.000 --> 00:05:51.000 Casey grease, saying it Marcella and Joanna, so we're really pleased to have you all here. I think that's your cue to go ahead and put your slides up. 00:05:51.000 --> 00:05:58.000 Thank you so much. 00:05:58.000 --> 00:06:00.000 Can you see this. 00:06:00.000 --> 00:06:05.000 Yeah, Okay. So thanks for having us. We're really happy to be here. 00:06:05.000 --> 00:06:11.000 This group came together through and associated colleges of the South grant. 00:06:11.000 --> 00:06:26.000 This summer, to work on creating modules that involve social justice and covert 19 for mathematics and data science classrooms, but these modules can be used in other classrooms. 00:06:26.000 --> 00:06:36.000 You know, all sorts of classes that we can cover which modules are good for what so different biology classrooms, and even some social science classrooms. 00:06:36.000 --> 00:06:48.000 So, With that, we were going to start by just talking about this issue that comes up when we start talking about these things which is should we be talking about coven 19 in the classroom. 00:06:48.000 --> 00:07:02.000 A lot of people are concerned that there's a, you know, ton of trauma around these events are, we don't know where our students are coming from we know they're coming from diverse backgrounds, and some of them have had family members died they themselves 00:07:02.000 --> 00:07:16.000 have been sick on our campus right now we have a ton of students who are infected and others who are quarantine so even currently there's just a lot of a lot going on that's distracting and hard for our students. 00:07:16.000 --> 00:07:26.000 But what I found from talking about this in the classroom is that students want to engage about this topic, they don't they don't need to be sheltered from it they're living it right now. 00:07:26.000 --> 00:07:34.000 As long as we do it in a way that respects their humanity and their own personal experiences. 00:07:34.000 --> 00:07:48.000 So we've come up with some ways to help you get into the topic. If you feel uncomfortable, bringing up in the classroom, and we're going to give you the link to our website that has our modules at the end there also some of them are on cubes hub. 00:07:48.000 --> 00:08:06.000 And we have just a beginning exercise that was taken was modified from something, Nathan Alexander did right Cena think. Yeah. And also I created a survey that I give to my class at the beginning of class and it's about the class you know do you have 00:08:06.000 --> 00:08:16.000 web access and so forth but at the end I say, is there anything I need to know about what you've been through with coca because we might be talking about coven in the classroom. 00:08:16.000 --> 00:08:28.000 And just this semester I gave out that survey and one of my students was saying thank you so much for asking this and here's what's going on, but I haven't had any students say to me, please don't talk about this, so I thought that was an easy enough 00:08:28.000 --> 00:08:46.000 you want to say something else about that other. I mean I have a similar experience with yours I haven't had any students saying no, I don't want to talk about it but in the assignment I made it a little bit more on the race, ethnicity socio economic status lack of health insurance, How do you think those things are affecting your experience 00:08:46.000 --> 00:08:48.000 with Corbett 19. 00:08:48.000 --> 00:08:52.000 That was my framework of this assignment. 00:08:52.000 --> 00:09:03.000 Yeah, and all of these activities that we posted are modifiable so you know they're open source please take them and adjust them to whatever you think would be best for your environment. 00:09:03.000 --> 00:09:13.000 And the other modules, we're going to talk about today are listed here. And we're going to start by, and these are all up on our website. 00:09:13.000 --> 00:09:19.000 And looking at some particular activity. 00:09:19.000 --> 00:09:22.000 So in case you want to take it away. 00:09:22.000 --> 00:09:31.000 So, yeah so Joanna said we we actually want to give you a little flavor what these activities are like so we're going to to do one. 00:09:31.000 --> 00:09:43.000 This one is, I think, really nice because you can do it in any classroom, even social sciences. And the idea is that you would do just a few minutes maybe 10 minutes at the beginning of the class but over several classes. 00:09:43.000 --> 00:09:53.000 Okay, so that's what we're going to do right now. And I would describe as very open. There's no right or wrong answer so it really invites participation. 00:09:53.000 --> 00:10:00.000 It has this like low floor high ceiling you could take the discussion really high depending on your students. And so hopefully you'll experience that. 00:10:00.000 --> 00:10:14.000 So what I'm going to do is put in two links right and they are two graphs this whole thing is about graphical representations that we found on the internet that have covered coven there's a whole wide range of this and try to just make sense of it through 00:10:14.000 --> 00:10:23.000 visual representations. So I'm going to give you a link of two representations. I'm going to put you in breakout rooms. Okay, about four people or so. 00:10:23.000 --> 00:10:35.000 And what I want you to do is first introduce yourselves, and then spend a little bit of time looking at the two graphs and just the questions I'm going to ask our What do you notice. 00:10:35.000 --> 00:10:50.000 And what do you wonder. Okay, so really open ended, you know not I mean, you know, very hopefully inviting questions. Right. And then, there's going to be a second link that is too passionate and what tablet is just a, it's a bulletin board digital bulletin 00:10:50.000 --> 00:11:20.000 board so as you have ideas that you wonder whether you notice, you'll type it in there and the way you do it on the bottom right is a little plus, you put the plus and I do ask me to you put like you know graph one, so people know which one you're talking 00:11:38.000 --> 00:11:42.000 So, I don't know how that was. 00:11:42.000 --> 00:11:47.000 But I'm hopefully you got a flavor of what this is like. 00:11:47.000 --> 00:12:01.000 The, the two graphs I'll just comment a little bit, the first graph was from there Georgia The like they're the it's from Georgia, and I think you I saw some people posting that if you noticed that the number of at the bottom, the scaling changed and 00:12:01.000 --> 00:12:07.000 so you could have had a huge increase, or you could have had a 50% increase, and the graph not change. 00:12:07.000 --> 00:12:13.000 Join if you click through that they have since corrected it, so this is on the website now. 00:12:13.000 --> 00:12:27.000 And the, the, going to the next one. 00:12:27.000 --> 00:12:40.000 So the one thing I want to say so. So, we have a mixture of graphs and we've kind of categorized them a little bit on this. And so some of them are things that have features that kind of maybe make you see things change your perception, you might say, 00:12:40.000 --> 00:12:53.000 been done incorrectly, but they change your perception. Some of these are just really complicated and have a lot of quantities going on maybe they're different types of graphs, so it really gives kids, students an opportunity to analyze and think about 00:12:53.000 --> 00:12:59.000 these, and then we have some that we specifically chose that have things about equity. 00:12:59.000 --> 00:13:14.000 And then, So not only do we have these graphs as as an activity like we just participated in, there's an assignment that you can give out to students, that's, that's part of the module as well. 00:13:14.000 --> 00:13:37.000 So, another module that we developed actually was motivated by the fact that when it comes to covert 19, even the most well meaning scientist has has to make certain decisions about how they present data, and the consumer of that information is going 00:13:37.000 --> 00:13:53.000 to interpret that information differently depending on who they are and so the motivation behind this other module we created actually came from this tweet from Donald Trump, about how the United States has has done better at testing than any other country 00:13:53.000 --> 00:14:06.000 in the world and so that made us think and I think it may probably made a lot of students think, what does it even mean to be good at testing, and how do we actually evaluate if the United States is doing a good job of testing. 00:14:06.000 --> 00:14:23.000 So, this module contains actually several activities that contains four activities and each of those activities it's broken up into smaller sections so for courses where maybe this module is huge, and you don't have enough time, or it doesn't support 00:14:23.000 --> 00:14:40.000 learning objectives you can certainly take pieces of this module and and insert it into your course. If you only have, say, time for half of the module, but the the first activity, invite students to think about how you might even go about testing or 00:14:40.000 --> 00:14:57.000 measuring how good a country is at testing for Kovats, we've got a ton of different statistics that that purport to measure the efficiency and efficacy of testing in different countries at but they all they seemingly tell us different stories so the first 00:14:57.000 --> 00:15:06.000 activity is about asking students to explore what they think might be a good way to measure whether a country is doing a good job at testing. 00:15:06.000 --> 00:15:21.000 And the second is actually related. The second activity in the module is actually quite similar to what Casey was talking about in our first module where students are asked to look at different graphs and different presentations of data of the same data 00:15:21.000 --> 00:15:33.000 and figure out what is being told in one representation, but not in one of the others and just one of those representations it's one of those graphs more effective at at conveying this data. 00:15:33.000 --> 00:15:50.000 In the third activity students dig into a particular statistic for measuring testing, efficiency, and that's the percent positive statistic that's the statistic that's generally used by the CDC and the World Health Organization so they'll dig into what 00:15:50.000 --> 00:16:03.000 that number means into what that number isn't telling us about the whole story. And then finally, students are asked to look at a bunch of the same data. 00:16:03.000 --> 00:16:19.000 The same statistic for different countries, and then actually try to give a persuasive argument in favor of this claim that the United States is doing a better job at testing, and then also to write the same argument but for the answer the same question 00:16:19.000 --> 00:16:28.000 but from the other perspective, what can, how can you present the data so that to claim that the United States is doing an actually very poor job at testing. 00:16:28.000 --> 00:16:44.000 So the whole idea of this module is that we have a ton of data surrounding this crisis we're in, and it's up to us as responsible consumers and producers of data to actually be really Cognizant and careful about the way we present the data. 00:16:44.000 --> 00:16:59.000 So this this module we, we can see fitting into a bunch of lower level math classes like finite math statistics quantitative literacy courses but we also see this fitting into a bunch of other courses across the curriculum. 00:16:59.000 --> 00:17:13.000 So bio classes, even like communications classes right this is all about how you can use data irrefutable information and use it to make whatever claim that, that you need to make. 00:17:13.000 --> 00:17:15.000 Yeah. 00:17:15.000 --> 00:17:21.000 Our second module. 00:17:21.000 --> 00:17:35.000 So this module, I developed for a summer program that is aimed at primarily underrepresented students who are interested in, mostly biology and pre med, and just STEM fields in general. 00:17:35.000 --> 00:17:38.000 And they do this. 00:17:38.000 --> 00:17:53.000 This summer program that kind of incorporates a bunch of lab presentations and you know wet lab work and so I really wanted to present this as a quantitative approach to the same scientific method that they might employ in any STEM field, because I'm 00:17:53.000 --> 00:18:06.000 all about kind of unifying the, you know math is always viewed as some separate scary thing so I want to make it as much like normalize it as much as possible to make it as much like what they might be accustomed to in their other classes, but also because 00:18:06.000 --> 00:18:15.000 the students they are coming to a predominantly white school and they are underrepresented, and we make it a big part of the program to talk about these issues. 00:18:15.000 --> 00:18:35.000 So in this lab, we watch a presentation from Dr Myra Ortega on the statistics of driving while black were hypothesis testing was used in a court case to disprove the idea that black and African American drivers were getting pulled over in a higher proportion 00:18:35.000 --> 00:18:48.000 than the rest of the population just due to random chance, and we take that same reasoning and apply it to some color coronavirus data, to see if there might have, whether it's random that there are higher proportion of people of color getting infected 00:18:48.000 --> 00:18:51.000 with coded. 00:18:51.000 --> 00:18:59.000 So it's, it works for students. These are students who are coming straight out of high school, some of them had statistics before some of them did not. 00:18:59.000 --> 00:19:08.000 But I just included some preliminary work where they watched a couple of short videos on p values and things like that so they were ready to go for the module. 00:19:08.000 --> 00:19:13.000 Next slide please. 00:19:13.000 --> 00:19:29.000 And as part of the activity, I asked them to do some follow up. Feedback just a short flip grid video talking about what was most meaningful or disturbing or surprising I feel like it's a nice thing to include if you're talking about topics that might 00:19:29.000 --> 00:19:35.000 be upsetting or controversial because it gives them a chance to vent a little bit and to process what they just did. 00:19:35.000 --> 00:19:48.000 And I got some really great feedback from them on this some several students mentioned that they had had statistics before and they've done hypothesis testing, but they never really understood what it meant. 00:19:48.000 --> 00:19:56.000 Or what it applied to until we actually applied it to something that they cared about, which was nice. 00:19:56.000 --> 00:19:58.000 Next slide. 00:19:58.000 --> 00:20:02.000 So another 00:20:02.000 --> 00:20:17.000 another module that I've used with actually some of the same students because I also have the pleasure of having them through cat throughout the calculus sequence is this modeling epidemics from influenza to covet 19. 00:20:17.000 --> 00:20:30.000 And this is a four part module which could be done you could do any one of the parts you don't have to do the entire thing, and I've used it in calculus differential equations, it can be adapted to different classes. 00:20:30.000 --> 00:20:42.000 And it's nice because it sort of walk students through the process of creating a discrete model of spread of infection in a spreadsheet that is highly accessible. 00:20:42.000 --> 00:20:58.000 Really for, you know, definitely not just for calculus students, and then going from there to continuous so they see how using taking limits of this discrete model takes you to the continuous si our model so it's a nice way to show how derivatives are 00:20:58.000 --> 00:21:08.000 useful. And then we actually do in a some of the later parts of the module we do some simulations and some collaborative Python code through Google co lab. 00:21:08.000 --> 00:21:23.000 And then we do model parameter exploration so in some classes I do the whole thing and others I just do a couple, so they work as standalone or not and it's nice because it's very collaborative they work together and Google sheets or do some collaborative 00:21:23.000 --> 00:21:34.000 coding, and I've done the coding with students you've never seen it before and they can still, they can still manage it's pretty, pretty simple. 00:21:34.000 --> 00:21:38.000 Oops, that I couldn't animation in there that I didn't intend to. 00:21:38.000 --> 00:21:43.000 OK, so the main thing I want to point out about all these modules is. 00:21:43.000 --> 00:21:55.000 I like to approach all this in a very exploratory way to make it as much like actual science as possible so it's not a case where, you know, I have the right answers, and they're coming to me and getting verification. 00:21:55.000 --> 00:22:08.000 Most of the questions are very exploratory and different people can have different results. And we also discuss the limitations of modeling the limitations of the case data, you know, be a skeptical about the work. 00:22:08.000 --> 00:22:19.000 So I always include questions like do you think your results are reasonable, we do things like they compare results to each other, versus coming to me and I say, Yep good or no bad, they just compare. 00:22:19.000 --> 00:22:33.000 I have them based on what the work they've done with influenza, try to come up with parameters for transmission and recovery that they think could work for coven, and they do it in a very exploratory way and see what they get and then we come together 00:22:33.000 --> 00:22:46.000 and talk about it versus, you know, nobody really knows what it is anyway right so that's how science works. And then we talk about yeah big part of it is talking about limitations and treating it as an exploration. 00:22:46.000 --> 00:22:51.000 So I got. 00:22:51.000 --> 00:22:53.000 Thanks. That was great. 00:22:53.000 --> 00:23:08.000 And I created well Marcel and I created the flat and the curve linear exponential s IR module and I've used this our versions of this in both my upper level computational modeling class, and also in my calculus to class. 00:23:08.000 --> 00:23:18.000 For my computational modeling class I converted it to our and had the whole exercise done in our, and at the end had them working with the SI our model and our. 00:23:18.000 --> 00:23:31.000 And for my calculus to class I actually added a beginning exercise where they solved the exponential de with certain parameters that we had been talking about from this data and then have them work through this exercise. 00:23:31.000 --> 00:23:49.000 This exercise uses the beginning data from March through like April or May of 2020, the data is at first extremely exponential and then very linear. And so if you slice it into two different sections you can work with them on doing an exponential fit 00:23:49.000 --> 00:24:04.000 to the data and then a linear fit and having them describe different growth rates, but then also this exercise focuses on what's a good model what's a good way to model something so if you try to model for very long. 00:24:04.000 --> 00:24:19.000 covert as an exponential growth model of I of course won't work out very well for very long because exponential growth blows up so fast, and also the linear data is and a good model and the reason these models are bad is because they're just fit, they're 00:24:19.000 --> 00:24:32.000 not taking any of the true biology the biological assumptions into account. And that's how we start presenting the car model as the first model that takes into account some of the disease transmission dynamics. 00:24:32.000 --> 00:24:46.000 And we actually use this really nice site. What happens next. That was created that has a ton of examples for the SPI our model, incorporating all sorts of interventions and and even vaccine at the end. 00:24:46.000 --> 00:25:00.000 And so they just explore that and try to answer some questions, some of them, you know concerned different groups and how this would affect different groups and how the parameters would be different depending on different situations. 00:25:00.000 --> 00:25:15.000 I also wrote a exercise for upper level, undergraduate differential equations class but I also use this for my computational modeling classic can really be modified for any type of modeling class. 00:25:15.000 --> 00:25:28.000 I used it in my computational modeling class they could either do a differential equations or an agent based type model, and it gives them. So all sorts of specifications for how this works for the whole semester. 00:25:28.000 --> 00:25:41.000 So, and I've used. I've used this in the past but this one is more specific to covert where you're taking you're extending some already created like sir coven model or trying to create some model on their own, the semester and Mike computational modeling 00:25:41.000 --> 00:25:57.000 This semester and my computational modeling class by this I mean for both of my groups decided to try to do the one group did an agent based model and one did a differential equations model and tried to figure out how different socio economic groups were 00:25:57.000 --> 00:26:17.000 being differentially affected by interventions, and one of the groups is continuing to work on it through a small grant from you are, and are now including vaccination, and Zanuck did you want to say something because I know you use this in your DS. 00:26:17.000 --> 00:26:29.000 if you can hear me right. I use it in a different setting, I did it in a 300 level course differential equation is mostly generally the first 300 level course that they're taking in my college. 00:26:29.000 --> 00:26:41.000 So one of the things they struggle is that like peer reviewed journal article where to find the peer reviewed journal article so I had to have a one on one meeting with them in the beginning before their pitch presentation. 00:26:41.000 --> 00:26:55.000 And don't be discouraged because after the pitch presentation I was like, oh no this is going to be because I put like 20% leads on this. I like this is going to be really a disaster but after the pitch presentation to the until the final presentation 00:26:55.000 --> 00:27:11.000 they put a lot of work in that they realized like oh this is something I can actually go talk about right. Some of them apply for a grant on diversity because they were looking at the covert 19 effects in minority groups, some of them that we have a social 00:27:11.000 --> 00:27:24.000 Innovation Hub, some of them apply to a project in the Social Innovation Hub so it turned out to be a really good project for them. But it took a lot of time to facilitate, I mean I took a lot of time on my part because it was the first time I was doing 00:27:24.000 --> 00:27:26.000 the project. 00:27:26.000 --> 00:27:37.000 Yeah, thanks. And I this semester in my modeling class I had them every week had the group's present someone else's coven model, either differential equations or an agent based model. 00:27:37.000 --> 00:27:53.000 And I think that really helped them to see like how you have to develop it. And also it was just really finally hear them talk about all these other models that were going on when they were trying to figure out what components to put into their models. 00:27:53.000 --> 00:28:07.000 So this is a module that I developed for my journal education course for non majors. I mean, they've been hearing about the basic reproduction number of what it means so it talks about the definition, it talks about other measures also it asked questions 00:28:07.000 --> 00:28:22.000 for them to look for other measures. And it kind of gives them an exercise to do scatter plot. Look at the real data concept of the exponential growth curve how it fits to the data and use the exponential growth factor and how you can use that to estimate 00:28:22.000 --> 00:28:36.000 the are not basically you're using the real data to talk about are not one thing I changed the second time I was using this module instead In the beginning I used the Milwaukee content dashboard data, because it was one of the few places they kind of 00:28:36.000 --> 00:28:51.000 gave the racial breakdown of the coverage 19 positive cases in the beginning, but the second time I was doing it, I already collected some data from the college is the dashboard it talks about the positive coverage 19 cases on on our campus. 00:28:51.000 --> 00:29:00.000 So I use that data and I make them estimate the basic reproduction number from that data. 00:29:00.000 --> 00:29:18.000 We have a few other modules that were mostly created by our collaborator cobre who couldn't make it today, and they're really beautiful modules, incorporating Tableau and our, where he looks at race and census data and coven positivity test numbers and 00:29:18.000 --> 00:29:35.000 help students to draw some interesting conclusions. These are modules eight and nine so some of the data was from Florida. And you can see that if you combine the Hispanic or Latino with the African American do data, you can see sort of the hot, where 00:29:35.000 --> 00:29:38.000 things are lighting up it kind of adds up to this map. 00:29:38.000 --> 00:29:55.000 That was one interesting feature. And then he also has a three prong map in Texas where the colors mean some things and then their numbers for each county and he's looking at census data and Texas, combined with the Copa data. 00:29:55.000 --> 00:29:58.000 So these are really nice modules too. 00:29:58.000 --> 00:30:01.000 So we created this website. Thank you. 00:30:01.000 --> 00:30:04.000 Marcel and Dana for linking to it, and. 00:30:04.000 --> 00:30:22.000 And the way that it's formatted as we tried to guess what course you would want to use these modules and and organize them this way. And so if you go into a course you'll see the different resources, and we've posted a guide, which is much like usually 00:30:22.000 --> 00:30:33.000 like the student handout and then there are also sometimes a spreadsheet with some data, or some other resources or co lab notebook. 00:30:33.000 --> 00:30:37.000 So feel free to use these and modify them. 00:30:37.000 --> 00:30:43.000 And that's it. So, now we were wondering. Yeah, so thank you. Thanks. 00:30:43.000 --> 00:31:02.000 Christian, so feel free to add them in the chat if you prefer to do that I'll try to monitor or if anyone wants to raise their hand and, and ask we can use that to 00:31:02.000 --> 00:31:18.000 ball folks are organizing their thoughts, the last, so the last one that we just saw the problem was in here, but which which course was that Euston 00:31:18.000 --> 00:31:28.000 think he was using it and data science intro to data science, I think it was for intro classes if there's not a lot up case you seem like you know better. 00:31:28.000 --> 00:31:43.000 So it's like a math liberal arts, because in modern mathematics, but it's math liberal arts, it's an intro, which could be considered a quantitative literacy class for non STEM majors. 00:31:43.000 --> 00:31:55.000 I actually did it he created one for South Carolina that I used. So I built on, I did, partly Florida and partly South Carolina. So it was really kind of him to make it for me to. 00:31:55.000 --> 00:32:09.000 Any questions from other folks Molly asked, Do you out to students ever bring you or mention articles means or graph that they haven't encountered on social media. 00:32:09.000 --> 00:32:26.000 I can answer that one. I also asked my students to keep a media log. So they record their covered 19 related news and any podcast any social media tweets so like the yesterday, one of my students, brought something and said oh dr z that's really related 00:32:26.000 --> 00:32:38.000 to you I'm like what do you mean Am I on the newspapers I know the author it's called Cena also. So there was like one of fake space and they think that's the very uncommon name which is like a very common name in Turkey, but that was like one of the 00:32:38.000 --> 00:32:52.000 that come up from the media look so they do especially I'm happy to share it, my assignment on the media log, but they do Qi make them collect information about comic 19. 00:32:52.000 --> 00:33:07.000 I mean I would say I found that most of my students were not looking and do not bring these and in fact when I mentioned things. Many of them were not did not have a lot of like I mentioned, Dr faculty and many of them were like, Who's that. 00:33:07.000 --> 00:33:22.000 So, um, but then other people did know some information but I don't know, it seems to me there's an argument to that these need to be in our classrooms. 00:33:22.000 --> 00:33:38.000 Yeah, for sure. I really like the overlap not just, of course in the social justice aspect of the, knowing what's going on in the world today and using math to investigate that better understand it, and all of those things, and these are critical issues 00:33:38.000 --> 00:33:55.000 especially the one about sort of misinformation or media spin. I'm curious. It sounds to me like you all are teaching these in very different contexts and you've developed them in different contexts Has anybody tried sewing them together in like a in 00:33:55.000 --> 00:34:03.000 one package that's sort of like a modeling and statistics for biology. for 00:34:03.000 --> 00:34:18.000 don't think we have tried that yet so I think we've taken, we, you know, we're very busy as everyone else this summer and so we were trying to get something out so that we could use some of it in each of our courses so I think we had an eye on our fall 00:34:18.000 --> 00:34:33.000 courses when we were designing them. And since then, some of us have designed other modules that we put together in a little book chapter and in there some more coming out so we haven't knitted together but when we put it all together, it was about, you 00:34:33.000 --> 00:34:50.000 know, 100 pages like this little mini book here, it would be nice to knit it all into one when I, when main course about stuff like modeling and this particular infectious disease because it was coming at it from a lot of different methods so it's a good 00:34:50.000 --> 00:35:03.000 idea. That's actually one thing I've kind of done is made like an infectious disease theme where I had these you rise students for the summer straight out of high school and we started with that hypothesis testing color of coronavirus and then I've used. 00:35:03.000 --> 00:35:08.000 I think three or so of the other modules. Over the course of our time together. 00:35:08.000 --> 00:35:14.000 So it's they are building, like a skill set in that area which is nice. 00:35:14.000 --> 00:35:21.000 And hopefully it will be coming up with more so that I can keep doing that for the semester. 00:35:21.000 --> 00:35:32.000 But it's nice to, it gives them something they feel almost like experts in which you know there is really nice to see. 00:35:32.000 --> 00:35:43.000 Um, any any other look I'm looking for other questions in the chat so I don't want to dominate all the time if anybody else has other questions. 00:35:43.000 --> 00:36:00.000 But I want to go back to the place that you started you all started with, which was getting ready to have these kinds of difficult, you know conversations in the classroom, and I was wondering if there was anything that that you hit, while teaching these, 00:36:00.000 --> 00:36:15.000 especially the ones that touch on social justice or understanding of racism and how you approach to handling this in the classroom. 00:36:15.000 --> 00:36:32.000 I, I teach in a predominantly white institution. So, generally like middle class or upper middle class so most of their experiences is like oh I'm not experiencing this but I understand other people are experiencing this issues. 00:36:32.000 --> 00:36:47.000 He went up. Also this is very interesting because when I did the, what are those, how are those things are affecting your education I find out so much about my students, and it might be a generation gap because I wouldn't share that much with people I 00:36:47.000 --> 00:37:02.000 don't know I just met my first week in addition to which I do appreciate right I mean, so I don't, I never bring up any of these issues in the classroom, of course, but if they want to come and talk to me one on one, I'm happy to do that they always, 00:37:02.000 --> 00:37:14.000 I mean they know that it's the same thing with Corbett 90 like I find out that when we get a notice for our students that are going to be virtual, I always write back and I said are you okay Do you need any help, then I realized they tell me all these 00:37:14.000 --> 00:37:26.000 things like oh, it's not me I'm not positive but I have a friend who is positive feels like, no I'm not asking any medical information I just want to make sure, audio okay that's the, that's the question like I'm not trying to find out more about what 00:37:26.000 --> 00:37:37.000 happened so I think that should be clear also in the writing assignments, it's not about your medical history about like how you are experiencing that thing. 00:37:37.000 --> 00:37:53.000 Yeah, I think. Same with me, I have the most I've had is just students who are just generally affected by the situation not as much. I mean I've had students say I've had family members die but none of them have said I don't want to talk about this because 00:37:53.000 --> 00:37:54.000 of that. 00:37:54.000 --> 00:38:01.000 But I've had a lot of anxiety, you know a lot of students with anxiety issues right now. 00:38:01.000 --> 00:38:09.000 But I found something similar does a nap where they seem like they're just openly talk about it in class you know and they get comfort from each other. 00:38:09.000 --> 00:38:22.000 Just like office so much right now and I'm, you know I having trouble dealing with whatever and they, it's not doesn't seem as quiet as it might have been when I was in college. 00:38:22.000 --> 00:38:31.000 Do you think part of that too is also the making space to talk about these issues that are currently affecting their lives. 00:38:31.000 --> 00:38:35.000 Would be nice right I hope 00:38:35.000 --> 00:38:52.000 I think giving them the feeling that you care about them goes a really long way for almost you know forget Koba just in general, I find that that's true, that you know you don't, you know, you're not giving them they I, I was really trying to get my students 00:38:52.000 --> 00:39:05.000 a sense last semester like grades aren't the thing, like I want you to learn some things and all the assessments are built like with you learning something in mind and there's all this space to collaborate and ask for help, and nothing is like a stressful 00:39:05.000 --> 00:39:19.000 time event. And I just felt like that was so much better than our usual like how I grew up, you better be ready for your test and you've ever be able to solve that problem in five minutes and if you can't, then you're going to fail and, and they've responded 00:39:19.000 --> 00:39:28.000 to that so well like the feedback I got was so positive about that, and I'm like this is what they're paying to learn right they're paying for me to teach them something. 00:39:28.000 --> 00:39:39.000 So, that seemed much like a better idea than what I was brought up under and just like what I've been doing in the past. 00:39:39.000 --> 00:39:52.000 And same for me like I just touched it at all this exams to this project type of assignments, and they reacted really well. 00:39:52.000 --> 00:40:07.000 Plus it relieved a lot of, you know, stress and worry about, do I have to catch people cheating which I never want to do either. If you make it so that there really isn't cheating because there's so much collaboration. 00:40:07.000 --> 00:40:15.000 There's not the same, that doesn't really exist in the same way, which is nice. 00:40:15.000 --> 00:40:28.000 Yeah, I've been seeing some chatter on various online places about stress in catching folks cheating and I love the idea of 00:40:28.000 --> 00:40:39.000 pedagogy is that leverage collaboration, inclusive pedagogy, you know, leverage collaboration and then also create environments that are less stressful for us as well. 00:40:39.000 --> 00:40:40.000 the same time. 00:40:40.000 --> 00:40:55.000 And, and then getting that student engagement in different ways and for different reasons. Because we're working through it together instead of trying to cram for some competition with each other. 00:40:55.000 --> 00:41:00.000 So I think that's a really nice reframing and opportunity. 00:41:00.000 --> 00:41:12.000 Pause because I feel like I feel like I'm talking too much and I'm not inviting other questions. 00:41:12.000 --> 00:41:24.000 Another question in the chat, along with this idea how do you do due dates are they're due dates know I can, I don't mind answering this one because I've tried a few different things. 00:41:24.000 --> 00:41:35.000 What I've done is sort of, well in my classes anyway he's a mastery grading format so everything is open to be revised all the time anyway but with labs. 00:41:35.000 --> 00:41:50.000 I never because it's exploratory and collaborative, I've kind of done a thing where there is a due date but it's not a hard due date and they need to submit something and we have like when people say do something like computer reproduction number four 00:41:50.000 --> 00:41:57.000 different states. I'll do something like in our class discussion I'll say you know once you have a value add it to the thread. 00:41:57.000 --> 00:42:03.000 And so everyone's chiming in and putting what they computed. And it makes it a little bit more of a. 00:42:03.000 --> 00:42:12.000 That's how they turn in their work it's just I'm contributing to the conversation thing, not a. I'm turning into the assignment thing and it makes them. 00:42:12.000 --> 00:42:22.000 I don't know seem a lot more feel a lot more like they're participating in real science. For one thing, which is nice and then they can compare their answers with each other and see if their works reasonable. 00:42:22.000 --> 00:42:35.000 And if they are turning in some kind of written assignment then I give them a soft deadline and then a certain amount of time after that they're able to revise and resubmit. 00:42:35.000 --> 00:42:38.000 I don't know if that was too complicated. 00:42:38.000 --> 00:42:47.000 My mind was maybe easier I had just due dates for a set instead of test I had assessments, but I guess it was sort of similar actually I didn't think about it. 00:42:47.000 --> 00:43:01.000 I said before, like if you want to send me your work ahead, I'll check it, like, I'll check and tell you if you're not doing it right so you, it's not just a test, you know, and some people took advantage of that and some data. 00:43:01.000 --> 00:43:10.000 But then it was do on a certain date, like a test 00:43:10.000 --> 00:43:12.000 is thinking. 00:43:12.000 --> 00:43:26.000 I don't know why wouldn't Marcel is talking she made me think about sort of the practices that folks are implementing and the change of practice and for some reason maybe think about sort of open science practice I mean one of the things I liked about 00:43:26.000 --> 00:43:42.000 your project is you so early conceptualize this as an open resource like that's what you're developing here you're developing something that that you want to be, you know, thinking about equitable insisted practices and so, you know, while you're talking 00:43:42.000 --> 00:43:51.000 about the the theory talking and talking about the nature of how science works are you talking about open science and conversations around that as well and. 00:43:51.000 --> 00:44:02.000 Is that part of what you're talking about here like the the big challenge of code 19 how people's information sharing is changing. 00:44:02.000 --> 00:44:16.000 I mean I think that's a really brilliant point I love your, you know love cubes because of that like it's such a great place where we don't have to all be generating for the first time, ideas about how to incorporate real applications in our classes. 00:44:16.000 --> 00:44:27.000 You know brilliance. Why should we have textbooks anymore It should definitely be downloadable and interactive resources right. That's such a good idea. 00:44:27.000 --> 00:44:42.000 For me, I guess I did that a little bit I didn't think of it that way wish I would have like I think that's a better broader way I did just like have open collaboration you know I had them have campus wire and Marcella did too I know where they could, 00:44:42.000 --> 00:44:53.000 you know, communicate, they were allowed to ask each other anything about anything about the homework or about the assessments. There was never a, you can't ask about that like please go work on this together and try to figure it out. 00:44:53.000 --> 00:45:06.000 You can also use the one that join in my experience, so I had a gr so my students are coming from every imaginable part of campus and in class they were really good about communicating and I think the activities that I did for these and they were great 00:45:06.000 --> 00:45:08.000 for stimulating discussion. 00:45:08.000 --> 00:45:14.000 If I did not put a structure in the assignment if I just invited people to work together, no one did. 00:45:14.000 --> 00:45:27.000 They are still so programmed to go home and do their work and turn it in as an individual, even when it said you're encouraged to do this and they had been working in classroom as a group, conceptualizing the project as a group, they still go and do it 00:45:27.000 --> 00:45:43.000 individually. I had to put structures in place to do rough drafts. If not, I got no one doing a rough draft, I had to put things in place that said, you need to work together in fact here you're getting graded on and or, and here's you have to document 00:45:43.000 --> 00:45:50.000 how you did. If I didn't put that in place my students. As soon as he left the classroom saw it as an individual. 00:45:50.000 --> 00:45:53.000 Traditional assignment. 00:45:53.000 --> 00:46:10.000 Yeah, that's a good point I should mention that I had my students and groups working on other pieces of the course. So they weekly had to turn in LA tech solutions by the way they were fine doing la tech and calc to thanks to the Bates manual, but they 00:46:10.000 --> 00:46:21.000 were fine during la tech but they were turned so they were working in groups and to do group presentations. And so I think they all had each other's phone numbers and were texting about other things. 00:46:21.000 --> 00:46:34.000 You know how do you do this problem, but I could see if if there wasn't like a group structure where they sort of had study groups, they wouldn't have felt that way either I think they would have gone individual yeah I'm super deliberate about groups, 00:46:34.000 --> 00:46:47.000 especially remote. They have, like every time they do group work during class they have to give a link to their Google Doc that they collaborated in to me at the end of class and it could be, you know, same thing when they're working on these labs, they 00:46:47.000 --> 00:46:54.000 have to, they kind of have to do we I guess we didn't mention that they need to work in groups. 00:46:54.000 --> 00:47:08.000 We just have one more question and I know it's really close to the top of the hour but something about deep, talk about how data are collected especially for covert 19, and the consistency of that data and when we just saw been sizes getting corrupted 00:47:08.000 --> 00:47:16.000 for color later on down the road and so consistency merging multiple data sets, do any of you tackle some of those issues in the classroom. 00:47:16.000 --> 00:47:20.000 Even if not directly and indirectly talking about it. 00:47:20.000 --> 00:47:35.000 Some of the modules that we created especially the ones surrounding pulling graphs off social media and stuff just inherently include that kind of discussion that Instructor Guide, kind of, facilitate those discussions to but I, the conversation at least 00:47:35.000 --> 00:47:49.000 in my courses is more about knowing that you can dig into the data and see where it came from, and that's something you should do when you're in, when you're consuming data right like it's so easy to just look at a graph make your immediate conclusions 00:47:49.000 --> 00:48:02.000 and then move on, when really you should look at the graph make your conclusions and then think a little bit about where the data is coming from and what decisions that producer of this graph or this information, had with the decisions they had to make 00:48:02.000 --> 00:48:06.000 when they were creating this platform. 00:48:06.000 --> 00:48:12.000 So that's definitely part of some of the activities and some of the modules. 00:48:12.000 --> 00:48:26.000 Great, thank you so much and I'm so excited to go through all of them and try them out, or share them with all sorts of other folks, and I want to thank those who stuck on to the very end and our audience and ask great questions and it's nice to see some 00:48:26.000 --> 00:48:39.000 familiar names.