Episode 55: Hollylynne Lee: Linking Data Science and Teacher Education

Dusty Jones: hello, and thank you for listening to the teaching math teaching podcast the teaching math teaching podcast is sponsored by the Association of mathematics teacher educators. 3 00:00:17.430 --> 00:00:22.140 Dusty Jones: The hosts are Eva Thanheiser Joel Amidon and me dusty Jones. 4 00:00:22.710 --> 00:00:34.680 Dusty Jones: Today we are talking with Dr Hollylynne Lee who has distinguished professor of mathematics and statistics, education and the senior faculty fellow in the Friday institute at North Carolina State University. 5 00:00:35.520 --> 00:00:46.260 Dusty Jones: We are talking to holly Lynn because of her experiences as a mathematics teacher educator and statistics teacher educator we'd also like to talk with her about the growing role of data science in education. 6 00:00:47.400 --> 00:00:52.440 Dusty Jones: Hollylynne welcome, could you take a minute to introduce yourself beyond what we've already shared. 7 00:00:53.340 --> 00:01:05.280 Hollylynne Lee: yeah sure so i'm holly Lynn and so i've been at nc state since 2000 i'm kind of one of those folks that goes a place and stays there and. 8 00:01:05.670 --> 00:01:21.510 Hollylynne Lee: So i've been my my whole PhD career at North Carolina State University and I absolutely love it and I do I at the Friday Institute, I actually direct a hub for innovation in statistics education and. 9 00:01:22.620 --> 00:01:33.810 Hollylynne Lee: Its high rise for short and we we do a lot of different projects related to statistics teacher education, as well as statistics and data science, education at the K 12 level. 10 00:01:35.340 --> 00:01:37.170 Dusty Jones: awesome awesome and. 11 00:01:38.640 --> 00:01:42.150 Dusty Jones: First question, we like to ask people is how did you start. 12 00:01:42.540 --> 00:01:45.750 Dusty Jones: Teaching math teachers or maybe why. 13 00:01:46.170 --> 00:01:54.270 Hollylynne Lee: yeah so I was getting my master's degree in the mid 1990s So yes, that's dating me and. 14 00:01:55.170 --> 00:02:05.160 Hollylynne Lee: I started doing professional development, with local school districts on how to use the graphing calculator and I was kind of one of those early adopters of the graphing calculator my own classroom. 15 00:02:05.670 --> 00:02:13.260 Hollylynne Lee: And so started, you know sharing that with with other teachers and it really inspired me to want to be a math teacher educator. 16 00:02:14.160 --> 00:02:23.580 Hollylynne Lee: And so, went on to my doctoral degree at university of Virginia and when I was there I got involved in a project with my advisor Joe garofalo. 17 00:02:24.390 --> 00:02:33.570 Hollylynne Lee: on developing teacher education curriculum materials for incorporating different technology tools like spreadsheets geometry sketchpad logo fathom. 18 00:02:34.500 --> 00:02:50.100 Hollylynne Lee: And you know part of that back in I think it was maybe 1998 we we did some of the first kind of pre conference workshops at a at an AMT conference i'm helping faculty learn how to use different technology tools. 19 00:02:50.580 --> 00:02:59.310 Hollylynne Lee: and think about it in their classrooms and from then on, I was just completely hooked of you know, being all in and being wanting to be you know involved in mathematics teacher education. 20 00:03:00.000 --> 00:03:04.650 Dusty Jones: awesome were you teaching high school when you were working on your master's degree or. 21 00:03:05.100 --> 00:03:06.420 Hollylynne Lee: i'm high school and middle school. 22 00:03:06.480 --> 00:03:07.710 Dusty Jones: High School and middle school okay. 23 00:03:07.770 --> 00:03:16.080 Hollylynne Lee: awesome so different different places, before getting going to my master's degree, I was had taught both high school and middle school yes. 24 00:03:16.380 --> 00:03:30.930 Dusty Jones: that's that's great one of the things we like to do with the podcast is let people who are new to teaching math teachers in on some of the secret sauce or the advice or the tips that we might give people so. 25 00:03:32.160 --> 00:03:36.720 Dusty Jones: What would you like to have known when you started teaching math teachers. 26 00:03:38.520 --> 00:03:40.440 Hollylynne Lee: I think it would be. 27 00:03:41.580 --> 00:03:47.070 Hollylynne Lee: Probably how hard it is to get sustained change in classrooms. 28 00:03:48.180 --> 00:03:56.370 Hollylynne Lee: And that you know you I became a teacher educator because I thought that I could reach more students by reaching the teachers and I do still think that's true. 29 00:03:56.700 --> 00:04:14.490 Hollylynne Lee: But I think that there's changes so slow within the educational systems and there's so many barriers that teachers face in in in in their daily work, and so really recognizing that change in practice, takes a long time. 30 00:04:14.910 --> 00:04:23.730 Hollylynne Lee: And that it's also that it is really you know that the change is not just the what happens in the classroom but it's related to what is valued on assessments it's. 31 00:04:23.850 --> 00:04:24.720 Hollylynne Lee: Connected to. 32 00:04:24.900 --> 00:04:27.870 Hollylynne Lee: policies that are at the district level or the State level and. 33 00:04:28.920 --> 00:04:35.730 Hollylynne Lee: Those all those all can impact change for positive ways and and you know impeded in negative ways. 34 00:04:35.790 --> 00:04:47.010 Hollylynne Lee: Right and I don't I did not have a good handle I did not have a good perspective on that when I first started, I thought oh i'm just gonna like teach these teachers i'm great things, and you know by next year everyone's going to be doing these things. 35 00:04:49.200 --> 00:04:54.390 Dusty Jones: What was what was, do you think some of the best advice that you received when you were starting out. 36 00:04:54.690 --> 00:05:03.360 Hollylynne Lee: yeah, so I think it was about don't be don't be afraid to create something new, or to do something in a new way um so. 37 00:05:04.020 --> 00:05:07.950 Hollylynne Lee: yeah I had you know I would come back in my doctoral degree. 38 00:05:08.370 --> 00:05:15.660 Hollylynne Lee: During my doctoral degree and i'd be talking to my advisors about the things that I was observing out out in the field, because I was supervising student teachers or whatever. 39 00:05:15.900 --> 00:05:28.830 Hollylynne Lee: or doing an internship and then we'd be like well if you don't like it then creates created a different solution and it was you know, it was this way of like stepping back of saying okay wait a minute use your brainpower. 40 00:05:29.340 --> 00:05:41.730 Hollylynne Lee: and your your creativity and your intuition, to create a different solution if you don't like what's actually going on, and I think that that has propelled me throughout have to really consider myself as a designer. 41 00:05:42.870 --> 00:05:50.160 Hollylynne Lee: and educational designer and that part of my work is is about designing solutions, whether it's for students or whether it's for teachers. 42 00:05:52.380 --> 00:05:53.010 Dusty Jones: that's cool. 43 00:05:53.280 --> 00:05:53.910 Joel Amidon (he/him/his): Can I jump in. 44 00:05:54.060 --> 00:05:54.810 Hollylynne Lee: Sure yeah. 45 00:05:54.960 --> 00:06:01.020 Joel Amidon (he/him/his): island with what what's something that stood out to you're like hey I want, I want to attack that thing that I just noticed. 46 00:06:01.410 --> 00:06:10.920 Hollylynne Lee: yeah, so I think the best there's two kind of big big things that happened in my career The first was during my doctoral degree. 47 00:06:11.340 --> 00:06:20.010 Hollylynne Lee: When I was working in classrooms elementary classrooms trying to teach some kids some things were on probability and I couldn't find some software that I. 48 00:06:20.880 --> 00:06:29.580 Hollylynne Lee: Did the things that I thought should be done, and so my my one of my advisor said, well then create your own software and I was like what. 49 00:06:31.920 --> 00:06:34.440 Dusty Jones: So so probability explorer was born. 50 00:06:34.470 --> 00:06:45.030 Hollylynne Lee: that's exactly right, you know, and so I created my own software to do what I thought it should do and and it ended up being used in my dissertation as well as some of the early studies that I did. 51 00:06:47.100 --> 00:07:00.570 Hollylynne Lee: As a faculty Member, and then I think the second one happened in more recently in 2014 when somebody said to me, you know you've been doing things with teacher education, how are you going to get that to scale, and you know. 52 00:07:00.600 --> 00:07:02.130 Hollylynne Lee: Would you like to create a mooc. 53 00:07:02.280 --> 00:07:03.750 Hollylynne Lee: And I was like no. 54 00:07:05.820 --> 00:07:06.720 Hollylynne Lee: sounds really hard. 55 00:07:08.250 --> 00:07:09.750 Hollylynne Lee: You know, a mooc being a massive. 56 00:07:09.810 --> 00:07:14.850 Hollylynne Lee: Open online course and for but, but specifically aimed at teachers and. 57 00:07:15.840 --> 00:07:23.220 Hollylynne Lee: You know I thought about it for a little while, and then I decided that, yes, I could take this pedagogy I could take that on and do it and. 58 00:07:23.640 --> 00:07:34.170 Hollylynne Lee: It was one of the most pedagogically challenging things i've ever done, but it was absolutely worth it, and and really shifted my focus in my in the last part of my most recent part of my career. 59 00:07:34.350 --> 00:07:39.720 Joel Amidon (he/him/his): yeah just taps into like here here's some agency in like you know. 60 00:07:40.110 --> 00:07:46.140 Joel Amidon (he/him/his): How you how do you exercise like wow I didn't I didn't know that was possible make my own program that's awesome make my own software. 61 00:07:46.260 --> 00:07:47.490 Joel Amidon (he/him/his): Right beautiful right. 62 00:07:50.490 --> 00:08:02.220 Dusty Jones: Thinking about this advice question, and maybe you've already come up, but to the people that you work with who are starting out or to others that you might not ever meet what what advice would you give to someone starting out as a. 63 00:08:02.820 --> 00:08:18.780 Hollylynne Lee: goddess or educator yeah you gotta get connected with your peeps you have to find you have to find your people, and you got to get involved, you know I mean I feel like I grew up through a empty with the many friends and colleagues many that are in this in this podcast. 64 00:08:19.950 --> 00:08:35.040 Hollylynne Lee: You know that that you know you make those connections and you find interesting things to do together and that you don't have to do things alone, so you know work with you find others work with work with people that you like and create new things and. 65 00:08:36.210 --> 00:08:48.360 Hollylynne Lee: You know, trust your instincts and that you, you became a math teacher educator because you had something to share so trust that and figure out the best ways to share it. 66 00:08:49.020 --> 00:08:58.590 Eva Thanheiser (she/her): So let me follow up on that let's assume you don't quite know how to find your peeps What would you recommend, on how to go about that. 67 00:08:59.340 --> 00:09:09.390 Hollylynne Lee: So I think that at the state level there's there's you know lots of different organizations, you know, even if it's the math your local and state level and CTS. 68 00:09:10.950 --> 00:09:24.270 Hollylynne Lee: And you'll find other teacher educators there within within that group coming to conferences like AMT and I know it can be nowadays it's kind of hard to do these these types of traveling but going to these conferences. 69 00:09:24.840 --> 00:09:30.810 Hollylynne Lee: makes a real difference and not being shy of just reaching out so you know if you see. 70 00:09:31.260 --> 00:09:42.450 Hollylynne Lee: An order if you read an article by somebody and you're in the idea, really, you know sticks with you and you want to have a conversation about it we're pretty friendly group, like reach out and ask to have a conversation. 71 00:09:44.190 --> 00:09:55.680 Eva Thanheiser (she/her): I want a second that because I think not just that the people love to hear that their work was read right so by reaching out you're actually doing them a favor. 72 00:09:55.830 --> 00:09:56.340 Hollylynne Lee: And yeah I. 73 00:09:56.370 --> 00:09:57.990 Eva Thanheiser (she/her): Do yourself a favor right. 74 00:09:58.320 --> 00:09:58.590 yeah. 75 00:09:59.940 --> 00:10:03.060 Dusty Jones: yeah that's that's i'll just third that if that's the thing. 76 00:10:04.770 --> 00:10:14.010 Dusty Jones: So i've i've done that, a few times and it's always i've always got good feedback from the people that i've said I really liked this article, this was this helped me think. 77 00:10:14.400 --> 00:10:16.950 Dusty Jones: Right and I always get good feedback with it. 78 00:10:16.980 --> 00:10:25.260 Hollylynne Lee: yeah yeah or you go to a session at a conference and you didn't have time to actually talk to the people afterwards and so reach out to them, you know when you get back home and. 79 00:10:25.740 --> 00:10:36.750 Hollylynne Lee: start a conversation I mean, I really do think that we all recognize that we are better better together, and that we all learn a lot from each other. 80 00:10:38.190 --> 00:10:40.350 Joel Amidon (he/him/his): just going to put an exclamation point and all that yeah. 81 00:10:40.500 --> 00:10:40.890 reach out. 82 00:10:44.490 --> 00:10:55.530 Dusty Jones: So holly Lynn you are active in in a lot of things you mentioned some some of those earlier So how do you how do you get things done what's what's. 83 00:10:56.340 --> 00:11:06.210 Dusty Jones: what's the process that that helps you kind of achieve those things or or take care of the minutia whatever what what's your what's your process look like. 84 00:11:06.450 --> 00:11:17.970 Hollylynne Lee: yeah so one is I work with people that have skills and perspectives that complement mind and not that they are identical to mine but that they they compliment me and. 85 00:11:18.930 --> 00:11:32.100 Hollylynne Lee: And we we learn how to to think together to produce together, and you know, the way that I work with colleagues now is certainly I think a little bit different than how I did in the beginning, because. 86 00:11:32.610 --> 00:11:45.780 Hollylynne Lee: I think early in my career, even just the technologies that we had to do collaborative work we're different, and you know my goodness, I remember many conversations with James tar through like an old archaic Skype account. 87 00:11:47.370 --> 00:12:00.120 Hollylynne Lee: When we're sitting in our offices and trying to you know, trying to have conversations together and and do our work, but we didn't have things like a Google Doc and so you know writing together was like passing back and forth a word document. 88 00:12:01.350 --> 00:12:05.010 Hollylynne Lee: But um you know, so we, I think we can get things done a little bit. 89 00:12:06.270 --> 00:12:14.490 Hollylynne Lee: more efficiently now, and you know and as you develop your your working relationship with others, you know whose skills. 90 00:12:15.030 --> 00:12:24.360 Hollylynne Lee: You know who's best at doing what and so whether it's designing curriculum materials or whether it is writing a paper or preparing for that class that you're co teaching. 91 00:12:25.260 --> 00:12:33.660 Hollylynne Lee: You know you draw upon each other's strengths and so that's you have to learn how to not believe that you have to do everything yourself. 92 00:12:38.970 --> 00:12:45.720 Hollylynne Lee: But I let a lot of things fall through the cracks to the busier I get there's a lot of things that don't get done. 93 00:12:46.890 --> 00:12:49.980 Hollylynne Lee: And then you take a sabbatical to try to catch up and get them done. 94 00:12:53.040 --> 00:13:04.110 Dusty Jones: yeah those those things in the cracks I need Sometimes I feel like I just need to you know get the pocket knife out and dig that out of the crack and then sometimes i'm like let's just leave that thing in the crowd. 95 00:13:04.140 --> 00:13:14.910 Hollylynne Lee: yeah oh yeah I have, I have finally like thrown out, you know some data and thrown out half written articles like you know what nobody wants to read that anymore just throw it away. 96 00:13:15.180 --> 00:13:23.040 Dusty Jones: I had this really great idea about trying to develop some time to use some sort of software to develop something to help develop a statistical idea. 97 00:13:23.460 --> 00:13:31.770 Dusty Jones: And it's sat in my brain for four years, and now does most does it, so I didn't even have to tell them i'm not going to let them know hey That was my idea. 98 00:13:33.000 --> 00:13:34.200 Hollylynne Lee: Because apparently a lot of you. 99 00:13:34.500 --> 00:13:35.160 Hollylynne Lee: Are not yeah. 100 00:13:35.220 --> 00:13:37.320 Joel Amidon (he/him/his): yeah great job dusty great yeah. 101 00:13:37.350 --> 00:13:38.250 Dusty Jones: Thanks thanks. 102 00:13:40.620 --> 00:13:51.330 Dusty Jones: So holly Lynn one of the reasons we asked you on here was to talk about data science, so can you can you tell us what data science is, can you define that for us. 103 00:13:51.480 --> 00:14:04.320 Hollylynne Lee: yeah yeah, so I think you need to think about data science as being, not a discipline but being multi discipline so it's a multi disciplinary field, and it combines skills and reasoning. 104 00:14:04.800 --> 00:14:09.270 Hollylynne Lee: in mathematics and and statistics, along with computational thinking. 105 00:14:09.780 --> 00:14:24.840 Hollylynne Lee: And some computer science skills to to really investigate and solve problems that are in a real world context or a different domains like medicine environmental science business education sports whatever social, political issues. 106 00:14:25.260 --> 00:14:33.300 Hollylynne Lee: You know data science exists because we have big problems to solve that produce big data. 107 00:14:34.140 --> 00:14:47.310 Hollylynne Lee: And that there are that there is data that can be collected in these different domains, and that we can harvest that data in smart ways to try to help find solutions to look for patterns and trends and. 108 00:14:48.060 --> 00:14:56.100 Hollylynne Lee: In to think about how how we can gain insights from that data to propose different actions and solutions. 109 00:14:58.320 --> 00:14:59.280 Hollylynne Lee: So eloquent. 110 00:15:02.940 --> 00:15:12.660 Hollylynne Lee: yeah but I mean, so you know it data scientists different than statistics, it includes statistics, but you know people will say that. 111 00:15:13.230 --> 00:15:20.010 Hollylynne Lee: i've heard statistician say that you know statistics is an art and science of data, and so a lot of statisticians. 112 00:15:20.340 --> 00:15:25.470 Hollylynne Lee: Have kind of made the claim well we're data scientists like we've been data scientists because. 113 00:15:25.770 --> 00:15:41.670 Hollylynne Lee: Those are the statisticians that are not necessarily living in theory they're not developing the statistical methods, because that is the science behind statistics using that mathematics and the probability concepts to create new statistical methods and we need that. 114 00:15:42.000 --> 00:15:51.930 Hollylynne Lee: But statisticians who are solving real problems and using the statistics tools are doing that art and science of data they are doing data science and. 115 00:15:53.430 --> 00:16:13.230 Hollylynne Lee: You know data science cannot be done without strong computing tools and that's a major difference and it, you know when we talk about data science in schools that has to be a major difference we don't do it without without strong technology tools. 116 00:16:16.470 --> 00:16:18.720 Eva Thanheiser (she/her): Like what kind of tools are you talking about. 117 00:16:19.410 --> 00:16:38.310 Hollylynne Lee: So certainly there are industry standard tools like Python and are and but there's also more friendly tools like tableau and spreadsheets and tools like Kodak and the common online data analysis platform that's what cut out stands for. 118 00:16:39.510 --> 00:16:49.860 Hollylynne Lee: You know, back in the back in the days we had tools like fathom and tinker plus and those tools were really helped us learn statistical ideas and explore data in new ways. 119 00:16:51.210 --> 00:17:01.710 Hollylynne Lee: You know, even though I started my career with helping teachers learn to use a graphing calculator the graphing calculator is a real improvement in making progress. 120 00:17:02.250 --> 00:17:20.310 Hollylynne Lee: In statistics and data science, education, it is a ubiquitous tool that that people have access to but it's not a tool that you use at all to do anything serious as far as exploring data you just can't look at large multivariate data sets on a graphing calculator. 121 00:17:21.000 --> 00:17:30.120 Eva Thanheiser (she/her): So I have used code up in my teaching and i'm wondering if you want to spend like two or three minutes just sharing what that is because it's a user friendly. 122 00:17:30.330 --> 00:17:34.770 Eva Thanheiser (she/her): It is thing that people could start using pretty much without. 123 00:17:35.880 --> 00:17:36.720 Eva Thanheiser (she/her): A lot of stuff. 124 00:17:37.080 --> 00:17:47.010 Hollylynne Lee: yeah yeah and it was purposely designed to be that way, so you know it comes out of the concord consortium and bill fencer, who was the original designer of fathom. 125 00:17:47.580 --> 00:17:56.070 Hollylynne Lee: That that was originally released back in 2000 you know started developing code up because he he knew that schools were moving towards. 126 00:17:56.370 --> 00:18:11.730 Hollylynne Lee: Not wanting to install software, you know that we needed we needed browser based tools and so that's kind of what what how Kodak was initially envisioned and it really is set up, so that you can import data. 127 00:18:12.930 --> 00:18:25.320 Hollylynne Lee: In a very easy to manage table format that kind of looks like a spreadsheet so you can have your rows and columns, but you can also rearrange that data to be hierarchical and format, so that you can see. 128 00:18:25.560 --> 00:18:31.410 Hollylynne Lee: You know, you could group your data, for example by states, so if you had data that was about different states, you could actually. 129 00:18:32.010 --> 00:18:38.610 Hollylynne Lee: With a simple simple move with a drag and drop move you could rearrange that table so that all of all of the. 130 00:18:39.210 --> 00:18:44.400 Hollylynne Lee: All of the data around Alabama were grouped together, you know it, and then you could do computations. 131 00:18:44.850 --> 00:18:56.610 Hollylynne Lee: Just for Alabama things like that, and then there's lots of different graphic tools in there, and one of the nice things about code APP is that, first of all there's a updates to it about every month and. 132 00:18:57.780 --> 00:19:07.650 Hollylynne Lee: And so, their their model of development is that they work with different research projects that that that need certain. 133 00:19:08.670 --> 00:19:20.130 Hollylynne Lee: Certain features built into code up and so that's how it expands, and so the whole Community benefits by this by this collective development and one of my projects actually had a partnership with Kodak. 134 00:19:21.150 --> 00:19:31.050 Hollylynne Lee: Two of my projects, actually, and so you know we've been able to be on the front line designing features of that and so it's a multi representational tool, where you can look at data in different ways. 135 00:19:31.320 --> 00:19:36.270 Hollylynne Lee: And they're all linked together and we certainly know from a lot of research even back in the days of looking at. 136 00:19:37.410 --> 00:19:52.860 Hollylynne Lee: Technology tools around learning your functions that if you can connect multiple representations together, it really assist the learner and thinking about that phenomenon, whatever that phenomenon might be in that mathematical or statistical object in new and interesting ways. 137 00:19:54.870 --> 00:19:59.670 Hollylynne Lee: Does that does that give the I would love to hear, if you have. 138 00:20:00.840 --> 00:20:03.210 Hollylynne Lee: Some insight into how you would describe code up. 139 00:20:04.950 --> 00:20:08.640 Eva Thanheiser (she/her): yeah to me code up was just like. 140 00:20:09.900 --> 00:20:12.720 Eva Thanheiser (she/her): Like i've also played with tinker plots just because. 141 00:20:12.750 --> 00:20:16.740 Eva Thanheiser (she/her): One of my good friends as a stats educator so you kind of get into these things. 142 00:20:17.250 --> 00:20:23.400 Eva Thanheiser (she/her): And code up is nice because, like you can think do things by maps and it's. 143 00:20:23.430 --> 00:20:24.270 Hollylynne Lee: Just oh yeah. 144 00:20:24.300 --> 00:20:31.170 Eva Thanheiser (she/her): But he doesn't have a lot of background like they have a census data they have the end they have data in there already so you don't. 145 00:20:31.320 --> 00:20:33.690 Eva Thanheiser (she/her): have to bring your own you can play with it. 146 00:20:33.900 --> 00:20:51.360 Eva Thanheiser (she/her): yep and i've used it in classes, where i've just said okay here, look at the educational data here and play with it and I I think it's just a really powerful tool and I kind of forgot about it so i'm so glad you mentioned it again because i'm like yeah that exists. 147 00:20:52.560 --> 00:21:05.580 Hollylynne Lee: yeah you know and and you're right that they they have these different capabilities, where they've they've built in samplers where you can actually draw data from census, you can draw data from the noaa and Oh, excuse me. 148 00:21:06.600 --> 00:21:09.330 Hollylynne Lee: You can there's a California. 149 00:21:10.530 --> 00:21:23.820 Hollylynne Lee: survey a health survey that they that they automatically can link into and just you so you have this large population of data and you could pick different variables that you want and go ahead and sample and bring in a random sample. 150 00:21:24.330 --> 00:21:41.250 Eva Thanheiser (she/her): And initialize is like visualization is something i've been really into because I do think that there's a communication problem as well, yes, that we have in like in math education and stats education in the news everywhere right like. 151 00:21:42.270 --> 00:21:42.930 Eva Thanheiser (she/her): So. 152 00:21:44.160 --> 00:21:51.210 Eva Thanheiser (she/her): finding ways to communicate math or large data in a way that people can wrap their heads around it. 153 00:21:51.540 --> 00:21:52.110 Eva Thanheiser (she/her): So I think. 154 00:21:52.380 --> 00:21:56.520 Eva Thanheiser (she/her): that's where the code up also is a good start. 155 00:21:56.940 --> 00:22:06.360 Hollylynne Lee: yeah I completely agree and it's, not just in math and statistics, I mean being being able to unpack and visualize data in social studies in. 156 00:22:07.110 --> 00:22:18.630 Hollylynne Lee: In economics in science, the science educators actually do a lot with code APP and a lot with with with data in their curriculum in many ways they're a little ahead of us. 157 00:22:19.440 --> 00:22:29.040 Dusty Jones: that's that's really awesome to hear I know the pre service teachers that i've worked with really as soon as they get into kota or like where has this been my whole life and. 158 00:22:29.880 --> 00:22:46.020 Dusty Jones: they're really excited to use it and I I like that that the students are the users of Kodak are making their own displays they're making decisions to make the display look like how they want to, or if it does something they're like wait that's not what I wanted. 159 00:22:46.350 --> 00:22:46.830 Dusty Jones: and 160 00:22:46.890 --> 00:22:51.780 Dusty Jones: They can adjust that instead of clicking on I want a scatter plot or I want a bar graph. 161 00:22:52.050 --> 00:22:52.320 Hollylynne Lee: yeah. 162 00:22:53.010 --> 00:22:53.940 Dusty Jones: yeah really cool. 163 00:22:54.210 --> 00:23:04.290 Hollylynne Lee: And yeah and one of the things that we we do a lot in my projects is we go into classrooms and use tools like code up with students and capture hours and hours and hours of video. 164 00:23:04.740 --> 00:23:16.350 Hollylynne Lee: And then we use those video to create teacher education materials so that you know, teachers, can have access to actually see my goodness, you know within the first 20 minutes of a student getting their hands on code up. 165 00:23:16.740 --> 00:23:25.350 Hollylynne Lee: Look what they were able to do and the kinds of conversations, they were able to have, and you know I think those types of videos, we all know that video cases. 166 00:23:25.890 --> 00:23:39.510 Hollylynne Lee: You can be incredibly powerful tool in teacher education, and you know, my group has been one of the ones that have contributed some of the some of the videos related to teaching statistics and classrooms and working with data. 167 00:23:40.230 --> 00:23:44.400 Eva Thanheiser (she/her): So, you know how we said in the beginning, just reach out to people you want to work with. 168 00:23:44.550 --> 00:23:46.860 Eva Thanheiser (she/her): I feel like I want to work with you holly and then. 169 00:23:48.570 --> 00:23:50.280 Hollylynne Lee: i'm all in a while let's go. 170 00:23:51.090 --> 00:23:53.370 Dusty Jones: Work with holly Lynn yes, you you do. 171 00:23:53.820 --> 00:23:54.570 Dusty Jones: Eva so. 172 00:23:55.560 --> 00:23:57.300 Dusty Jones: Having said that, having having done. 173 00:23:57.570 --> 00:24:00.390 Dusty Jones: Other projects with her together it's been fantastic. 174 00:24:01.980 --> 00:24:02.520 Dusty Jones: holly Lynn. 175 00:24:03.720 --> 00:24:07.590 Dusty Jones: Going back to the data science, then, and we never really left it but. 176 00:24:07.800 --> 00:24:15.720 Dusty Jones: yeah, how can, how can math teacher educators incorporate data science into their work or what what do we need to be thinking about. 177 00:24:15.930 --> 00:24:23.520 Dusty Jones: yeah Do I need to you know squeeze two things out of the syllabus so I can put data science in there, what what does this look like. 178 00:24:23.580 --> 00:24:35.730 Hollylynne Lee: yeah so first of all, it is hard, because all of our teacher education programs are set up differently at different institutions it's the it's the beauty and the pain of math teacher education is there is no one formula that. 179 00:24:36.480 --> 00:24:48.720 Hollylynne Lee: That exists across all different institutions, and so it really you know it really does depend on what you need to squeeze as you were talking about dusty but, but just first of all, just some awareness that. 180 00:24:49.740 --> 00:24:56.760 Hollylynne Lee: You know, statistics and probability have been part of the math curriculum for a long time, but they get left out. 181 00:24:57.180 --> 00:25:12.900 Hollylynne Lee: by many teachers in K 12 settings for a variety of different reasons, but they also get left out by our colleagues in mathematics teacher education if you're not comfortable with the topic, and you are designing your class you're not going to address it and. 182 00:25:14.400 --> 00:25:27.120 Hollylynne Lee: But at the same time, there are several states like Oregon like California like Virginia that are creating different high school pathways that actually include courses and data science. 183 00:25:27.600 --> 00:25:33.030 Hollylynne Lee: And as they get and get guess who they're going to expect to teach those courses. 184 00:25:33.690 --> 00:25:43.830 Hollylynne Lee: it's your math teachers, because there is no certification for being a data science teacher or being a statistics teacher, by default, they say, well let's just give it to the math teachers. 185 00:25:44.190 --> 00:25:53.460 Hollylynne Lee: So your mat your future math teachers in several years are going to be in high school settings that are going to have these. 186 00:25:54.570 --> 00:26:03.750 Hollylynne Lee: That the ap statistics curriculum is has been increasing well those students enrolling in our curriculum has been increasing drastically. 187 00:26:05.160 --> 00:26:14.430 Hollylynne Lee: As well as ap computer science and data scientists really kind of the merging of those two and giving it as as as an accessible option. 188 00:26:14.730 --> 00:26:30.210 Hollylynne Lee: to everybody, so that you don't have to take an ap class to be able to actually do things with computers and statistics and data and whether it is you know the States that are creating these pathways in this specific course or it's just. 189 00:26:31.440 --> 00:26:47.760 Hollylynne Lee: States finding new ways to actually bring in more data and data science like things, even if they don't call it data science into the curriculum and I think that's The key thing is that it may not be called data it doesn't have to be called data science to look like data science. 190 00:26:52.470 --> 00:26:52.860 Dusty Jones: cool. 191 00:26:54.330 --> 00:26:55.260 Eva Thanheiser (she/her): So so. 192 00:26:55.620 --> 00:27:09.120 Eva Thanheiser (she/her): This like really interesting and convenient current divergence between teaching math for social justice and data science. 193 00:27:09.300 --> 00:27:20.820 Eva Thanheiser (she/her): Yes, and so I think we're like tackling a lot of things that the field is currently trying to figure out how to do by paying attention to these things. 194 00:27:20.880 --> 00:27:32.760 Hollylynne Lee: yeah and I i'm glad you brought that up because I don't think I yet answered these questions about what what should math teacher educators do to incorporate data science, you know in prepare these future teachers. 195 00:27:33.060 --> 00:27:52.380 Hollylynne Lee: And certainly you know I think all of us are wanting to attend to more equity and social justice issues with our with our future teachers and exploring larger data sets around environmental science around climate change around housing and food insecurity. 196 00:27:53.550 --> 00:28:02.670 Hollylynne Lee: are wonderful ways, especially Eva like bringing in the idea of connections with geography in place based and and how different. 197 00:28:03.870 --> 00:28:08.910 Hollylynne Lee: Different places in our communities might have different access to. 198 00:28:09.480 --> 00:28:27.000 Hollylynne Lee: Different resources, and you can see that visualize through data, and I think it's a it's a great way to bring those ideas in and be addressing them, as well as introducing your teacher education teacher education students to the ideas of solving problems through data. 199 00:28:29.070 --> 00:28:45.240 Hollylynne Lee: So you know you, you have to you do have to think about what to push out and and that's it's not an easy thing I do think you should be talking with your colleagues in the statistics and math department, if you don't live in a statistics and math department. 200 00:28:46.290 --> 00:29:07.560 Hollylynne Lee: So that that the courses that that the teachers are taking that there that are content focused are also including a good dose of data science concepts and that you are making room in your teacher ED curriculum arm for addressing issues around data literacy and. 201 00:29:09.090 --> 00:29:19.350 Hollylynne Lee: and improving the learning of statistics and data science, I would say that most I mean dusty I think you would probably agree with me that that and you might not so I shouldn't say that but. 202 00:29:19.980 --> 00:29:20.400 Dusty Jones: we'll see. 203 00:29:21.000 --> 00:29:33.780 Hollylynne Lee: we'll see that that what what we do and what would a lot of things that are being promoted as good data science, education, statistics educators, have been doing feel like they've been doing for a while. 204 00:29:34.470 --> 00:29:43.230 Hollylynne Lee: I do agree with that and um but it's not the typical thing that happens when we when we say Oh, we have to teach statistics. 205 00:29:43.560 --> 00:29:48.150 Hollylynne Lee: You know what typically happens is you give students a list of numbers that has no content. 206 00:29:48.480 --> 00:29:58.950 Hollylynne Lee: And you say compute this you know and tell me what the mean is, or you know, create this box plot and just report out, you know the IQ are and. 207 00:29:59.010 --> 00:30:09.210 Hollylynne Lee: yeah you know or plot these plot these these two by various you know, two variables and give me their aggression model and interpret the interpret the meaning of the slope. 208 00:30:09.360 --> 00:30:10.200 Hollylynne Lee: And that's it. 209 00:30:11.010 --> 00:30:11.460 yeah. 210 00:30:12.840 --> 00:30:21.000 Eva Thanheiser (she/her): I found that in my courses I teach the content courses for producers elementary teachers and I happen to be in a math department. 211 00:30:21.810 --> 00:30:34.620 Eva Thanheiser (she/her): But even understanding what the mean is interpretations of domain is mind boggling right like what Where does this number, like the added all to get on divide. 212 00:30:34.950 --> 00:30:53.010 Eva Thanheiser (she/her): By are we doing that and what does that represent is the other ways to get to it so it's that's The other thing I think is important it's not just this thing that is like you get to when you have tons of data it's actually like it starts really early on, when we make sense of concepts. 213 00:30:53.460 --> 00:31:00.510 Hollylynne Lee: Right right, but I would say that the math educators tend to be drawn to the kinds of. 214 00:31:01.260 --> 00:31:10.710 Hollylynne Lee: ideas that you were just talking about Eva of you know, really understanding the mathematical aspect of the concept of the me and because that's the. 215 00:31:11.100 --> 00:31:21.030 Hollylynne Lee: that's what we are comfortable with but really trying to know and interpret the mean, along with other measures and knowing that you know the mean doesn't tell you. 216 00:31:21.030 --> 00:31:33.270 Hollylynne Lee: Everything you really have to understand something about the distribution behind it and the sample of data in order to actually effectively use it, and if we don't ever get our students there then they're still living in the math world. 217 00:31:33.300 --> 00:31:35.340 Hollylynne Lee: of understanding the concept of the me. 218 00:31:35.880 --> 00:31:36.720 Eva Thanheiser (she/her): Let me give you. 219 00:31:37.650 --> 00:31:44.910 Eva Thanheiser (she/her): A tidbit of information that I learned way too late in life there is actually a measure called the mad score. 220 00:31:45.600 --> 00:31:47.880 Eva Thanheiser (she/her): That you can make sense of that helps you. 221 00:31:47.880 --> 00:31:55.560 Eva Thanheiser (she/her): understand the distribution, because I refuse to teach standard deviation I was like we can't make sense of that I can teach this. 222 00:31:56.040 --> 00:32:07.710 Eva Thanheiser (she/her): And I complain to my stats educated and they're like, why are you not teaching the math and i'm like the one year so there's things that are out there that are really useful it needs to make sense. 223 00:32:08.370 --> 00:32:13.500 Hollylynne Lee: Right right there are statistical tools and the mean absolute deviation is one of them. 224 00:32:14.580 --> 00:32:22.200 Hollylynne Lee: And instead of using a standard deviation we can use the absolute deviations of how each data point varies from that mean yeah. 225 00:32:22.470 --> 00:32:34.290 Eva Thanheiser (she/her): And that we can like understand what that matters right where's this and i'm doing a formula, the standard deviation is really hard when you're an elementary educator. 226 00:32:34.800 --> 00:32:35.190 Hollylynne Lee: mm hmm. 227 00:32:36.600 --> 00:32:43.650 Joel Amidon (he/him/his): I got a question for you how it went so yeah when I first started teaching high school mathematics 2002. 228 00:32:44.430 --> 00:32:52.980 Joel Amidon (he/him/his): We we use the core core plus curriculum, yes, had you know the different strands mixed in the kind of philosophy with for those that are familiar with one of the. 229 00:32:53.370 --> 00:32:58.620 Joel Amidon (he/him/his): The nsf funded curricula and actually we were field testing the second iteration of that curriculum so. 230 00:32:59.130 --> 00:33:08.460 Joel Amidon (he/him/his): Trying to identify like if you're going to take one last math class What would you take so we had some algebra some geometry some statistics and probability and even some discrete mathematics, it was all incorporated in. 231 00:33:08.910 --> 00:33:17.130 Joel Amidon (he/him/his): And it felt like I mean if, when I went back to Grad school and I was doing my Grad level statistics class, we were doing some of the same things that I was teaching to my sophomores. 232 00:33:17.880 --> 00:33:18.600 Joel Amidon (he/him/his): And they're. 233 00:33:18.660 --> 00:33:25.020 Joel Amidon (he/him/his): really getting the difference between what you know what How does statistics and probability feed into each other and looking at the different measures and things. 234 00:33:25.260 --> 00:33:35.820 Joel Amidon (he/him/his): You know even meet absolute deviation we were talking about that way, but you know, as in my freshman number class and software level class that I was teaching and so it felt like that was a step forward with the end so. 235 00:33:37.110 --> 00:33:45.420 Joel Amidon (he/him/his): It felt like and then I think the the high school, I was teaching a step back from that curriculum where was like though that's where we need to be going like to have that. 236 00:33:46.080 --> 00:33:51.600 Joel Amidon (he/him/his): To see how all those things put together how does algebra institutes and, probably, how did they all fit together, and how can we. 237 00:33:52.200 --> 00:33:59.370 Joel Amidon (he/him/his): Ask these big questions like we were looking at some some data sets that were actually out in the world are we created some of our own data sets and. 238 00:34:00.030 --> 00:34:06.060 Joel Amidon (he/him/his): So I don't know like what, what are the things that we need to be doing, as you know, teacher educators, to think like. 239 00:34:06.270 --> 00:34:16.440 Joel Amidon (he/him/his): How do we keep some of these things going I think these are these are good things these this this progress that we're making like data science is being put out there, like, how do we add fuel to that fire. 240 00:34:17.220 --> 00:34:26.760 Hollylynne Lee: Well, I think that you have to engage your teacher educators, with those types of projects, I mean they have to see that they can do a larger data investigation. 241 00:34:27.690 --> 00:34:41.610 Hollylynne Lee: And you know to solve a real problem and really get immersed in that and see wow this is this is exciting you that you know, realizing you know what this is probably the lessons in your class that your kids are not going to ask why am I going to ever use this. 242 00:34:41.970 --> 00:34:48.120 Hollylynne Lee: Because they're going to know they're doing it right, then you know they're there they see the real world applicability of it. 243 00:34:49.170 --> 00:35:01.350 Hollylynne Lee: And so I think they have to experience that and they have to then become advocates and comfortable with going into the going back into their classrooms and and seeing that. 244 00:35:02.250 --> 00:35:12.090 Hollylynne Lee: You know the curriculum sequence put statistics you know the statistics lessons towards the end of the year, I need to make sure I save time for those and knob squeeze them out, you know. 245 00:35:12.390 --> 00:35:21.510 Hollylynne Lee: Or maybe I need to put in, you know, maybe I need to advocate for moving them to the beginning of the year, because they can lay a strong foundation for other ideas right. 246 00:35:21.690 --> 00:35:23.760 Eva Thanheiser (she/her): actually want to add in here I. 247 00:35:23.760 --> 00:35:33.180 Eva Thanheiser (she/her): Just was sharing with a bunch of people that I changed the curriculum in one of my courses to attach to. 248 00:35:34.080 --> 00:35:46.410 Eva Thanheiser (she/her): Two measures of Center hearse because I teach at most everything in context now and to have you have to have a really good understanding of what mean median and mode mean. 249 00:35:47.160 --> 00:35:56.280 Eva Thanheiser (she/her): To really understand things, and so in some sense there's an argument for pulling it up front, which will help you understand, other things better, as well. 250 00:35:56.640 --> 00:36:11.280 Hollylynne Lee: Right right in because it's not just about the concepts of statistics and probability and data science it's about having a curious and creative and Problem Solving and perseverance, all of the different. 251 00:36:12.120 --> 00:36:22.590 Hollylynne Lee: You know the the soft skills, the disposition of skills that we want our our teachers and our students to develop and you can snatch you naturally do that when you're engaged with the data investigation. 252 00:36:23.430 --> 00:36:26.670 Eva Thanheiser (she/her): And we want them to understand that world and. 253 00:36:29.100 --> 00:36:36.450 Eva Thanheiser (she/her): The news and all of that, and I know dusty is probably looking at us, because we have to wrap up, but this was such a good conversation. 254 00:36:37.290 --> 00:36:38.940 Dusty Jones: No, no nope just looking around. 255 00:36:41.070 --> 00:36:45.540 Dusty Jones: holly Lynn, can you tell us a little bit about data science for everyone. 256 00:36:45.990 --> 00:36:47.040 Hollylynne Lee: yeah so. 257 00:36:47.670 --> 00:36:58.740 Hollylynne Lee: yeah so data science for everyone.org is a relatively new initiative that you know lots of lots of good people from across the country have really gotten together to think about. 258 00:36:59.310 --> 00:37:12.000 Hollylynne Lee: How to promote the ideas that that we need data science in the K 12 curriculum, that there are some resources out there we're not starting from scratch, there are lots of organizations that have been. 259 00:37:12.630 --> 00:37:21.870 Hollylynne Lee: Like I said it may not have been called data science before but they're they've created a lots of different materials and so they're very much of an advocacy group where. 260 00:37:22.680 --> 00:37:30.270 Hollylynne Lee: they're they're writing position statements they're getting involved in different professional organizations to help get the word out. 261 00:37:30.570 --> 00:37:42.120 Hollylynne Lee: There they've got a wonderful website that allows you to search for different projects and different activities, so that you can find things if you want to find curriculum material for for. 262 00:37:43.380 --> 00:37:52.560 Hollylynne Lee: Improving your improving your own practice as a K 12 teacher or as a teacher educator there's different resources that you can find there, so, in some ways it's kind of a hub of. 263 00:37:54.000 --> 00:38:06.450 Hollylynne Lee: Resources but, for example, they just they just closed out a lesson plan contest, so you know they were inviting people to create lesson plans that that were around data science and, eventually, you know when. 264 00:38:07.110 --> 00:38:13.770 Hollylynne Lee: That those submissions are closed, but eventually those are going to be on their website and accessible, so I think they're trying to um. 265 00:38:15.120 --> 00:38:19.170 Hollylynne Lee: To be a place for people to go and look for and to be be an advocate for this. 266 00:38:19.530 --> 00:38:27.690 Dusty Jones: yeah it's a will put the URL in the show notes it's data science for everyone.org but the four is the numeral four. 267 00:38:27.810 --> 00:38:29.130 Hollylynne Lee: Number four right yeah. 268 00:38:29.160 --> 00:38:30.030 Hollylynne Lee: Right yeah. 269 00:38:31.410 --> 00:38:36.420 Hollylynne Lee: So can I can I put a quick plug in for some of the materials that i'm currently working on. 270 00:38:36.510 --> 00:38:39.690 Dusty Jones: I was just going to ask you, what do you have to promote holly Lynn so please. 271 00:38:39.690 --> 00:38:48.990 Hollylynne Lee: Yes, yeah so you know for years i've been working with my colleagues rick Hudson and stephanie Casey and bill fender and G moment chica. 272 00:38:49.710 --> 00:38:59.190 Hollylynne Lee: With with me at the Friday Institute and we have a project called esteem, which stands for enhancing statistics teacher education through E modules. 273 00:38:59.520 --> 00:39:08.400 Hollylynne Lee: And it started in 2016 and and so we're now at a point where we have several different modules that we offer up for free we're giving them away. 274 00:39:09.480 --> 00:39:22.500 Hollylynne Lee: they're already packaged in learning management system, so we tried to make it easy for teacher educators, to come to our site to be able to and i'll share that link, so you can put it in the the materials dusty. 275 00:39:22.710 --> 00:39:33.840 Hollylynne Lee: Sure, but they come to us, they come to our site they log in, and then they can download our materials and import them into their own learning management system, whether it's moodle blackboard canvas. 276 00:39:34.110 --> 00:39:41.280 Hollylynne Lee: And then they can change them to fit in with whatever other materials they're using in their course and so we're trying to make it. 277 00:39:42.120 --> 00:39:49.980 Hollylynne Lee: portable and easy for teacher educators to to you know to come and learn and to get good materials to use in their course. 278 00:39:50.460 --> 00:40:03.780 Hollylynne Lee: The other thing that we've got going on, so I mentioned earlier about doing moocs so this past summer we we launched another mooc called amplifying statistics and data science in classrooms. 279 00:40:05.100 --> 00:40:12.450 Hollylynne Lee: And it's going to be up there forever, so we decided to do kind of it do it in an on demand format where. 280 00:40:13.200 --> 00:40:25.350 Hollylynne Lee: Teachers can teachers can come in and we've got two different modules that have five different units, each in there and they can learn at their own pace to improve their practices in teaching statistics and data science. 281 00:40:25.860 --> 00:40:34.110 Hollylynne Lee: it's completely free and it's available for teachers, I even had a teacher educator this fall using it with their methods course. 282 00:40:34.710 --> 00:40:37.890 Hollylynne Lee: You know, so a friend of mine contacted me and said hey you know, I think. 283 00:40:38.610 --> 00:40:45.780 Hollylynne Lee: it's kind of you know, a pieces I know is missing in my course and what do you think about me having my pre service teachers sign up for your mooc and. 284 00:40:46.110 --> 00:40:53.040 Hollylynne Lee: You know they've got to show me their certificate at the end that they've actually completed these things i'm like sure come on in so. 285 00:40:53.790 --> 00:40:58.620 Dusty Jones: that's awesome and that gives them kind of a head start on professional learning once they're. 286 00:40:58.620 --> 00:41:00.360 Dusty Jones: In a classroom like what can I do. 287 00:41:01.080 --> 00:41:03.210 Dusty Jones: Right, can I, how can I do some of this that's awesome. 288 00:41:03.390 --> 00:41:04.110 Dusty Jones: What else you got. 289 00:41:04.590 --> 00:41:18.570 Hollylynne Lee: Well, the we have a current nsf project called instep and we have a landing page right now but there's nothing behind the landing page so it's it's in step with data.org and we're building a. 290 00:41:19.110 --> 00:41:28.950 Hollylynne Lee: What we've learned a lot from six years of doing moocs of how teachers actually really wants to personalize their learning related to statistics and data science, education. 291 00:41:29.250 --> 00:41:40.320 Hollylynne Lee: So we're we're designing a personalized mobile platform that we're putting together different experiences for teachers and that they can go in and basically designed their own adventure. 292 00:41:41.280 --> 00:41:51.630 Hollylynne Lee: and choose what you know, having enough material in there and then packaged in different ways, that they can see what they need and the areas of pedagogy that they would like to. 293 00:41:51.990 --> 00:41:59.160 Hollylynne Lee: to work on, and they can go in and work on a module specifically for that so, for example, if they really wanted if they've been teaching. 294 00:41:59.610 --> 00:42:04.380 Hollylynne Lee: statistics and data science for a while they feel very comfortable with a lot of ideas, but they want to really improve. 295 00:42:04.800 --> 00:42:12.960 Hollylynne Lee: On their understanding of how to go get good data site data sets and to use different technology tools they could go and use, you know do some modules Bob. 296 00:42:13.500 --> 00:42:27.450 Hollylynne Lee: specifically about that, but if they're just starting, they could start with a data doing a data investigation themselves where they're diving into a real context, using code APP and kind of learning learning on their own of. 297 00:42:28.200 --> 00:42:33.120 Hollylynne Lee: How to engage with data and go and start working on two modules of how to improve their pedagogy. 298 00:42:34.740 --> 00:42:41.100 Dusty Jones: That is awesome so in the show notes will have links to these things people can choose their own adventure with. 299 00:42:41.160 --> 00:42:46.320 Dusty Jones: yeah one of some of the many things that we have there yeah so much holly when that's great yeah. 300 00:42:46.470 --> 00:42:52.710 Hollylynne Lee: Can I give one more plug and I know I know we're running out of time, but this is related to something that Eva was talking about related to. 301 00:42:53.340 --> 00:43:05.520 Hollylynne Lee: Thinking about data literacy for all for all and and thinking about equity and social justice issues, one of my projects called writing data stories is a partnership with some science educators, Michelle wilkerson. 302 00:43:06.390 --> 00:43:14.100 Hollylynne Lee: At uc Berkeley and we've created some short activities that are kind of like number talks. 303 00:43:15.600 --> 00:43:16.170 Hollylynne Lee: That. 304 00:43:17.190 --> 00:43:31.350 Hollylynne Lee: That use the graphs from media so we're kind of going off the idea from the New York Times of what's what's going on in this graph and but we specifically asked questions that have a social justice. 305 00:43:32.610 --> 00:43:39.030 Hollylynne Lee: lens to them to get students to really understand who is represented in this data who's not represented in this data. 306 00:43:39.420 --> 00:43:46.140 Hollylynne Lee: And what would that mean, as far as my interpretation of how I should use this data and how how useful it is. 307 00:43:47.100 --> 00:43:49.530 Hollylynne Lee: in getting them to really unpack visualizations. 308 00:43:49.980 --> 00:44:00.120 Hollylynne Lee: Through an equity and social justice lens and those are called data Bytes and we have a set of them that we that we are giving away for free and they're already in like Google slide format. 309 00:44:00.390 --> 00:44:08.790 Hollylynne Lee: So that a teacher, can you can use them right there with their students and we have them fully bilingual so they're they're written in both English and Spanish. 310 00:44:11.220 --> 00:44:12.120 Dusty Jones: that's awesome. 311 00:44:13.800 --> 00:44:14.190 Dusty Jones: So we. 312 00:44:14.250 --> 00:44:14.850 Hollylynne Lee: i'm done. 313 00:44:14.910 --> 00:44:15.750 Hollylynne Lee: i'm done promoting. 314 00:44:16.140 --> 00:44:19.200 Dusty Jones: i've been finding links i'll keep i'll double check these links. 315 00:44:19.410 --> 00:44:20.790 Hollylynne Lee: Make sure to type everything right. 316 00:44:21.060 --> 00:44:22.530 Dusty Jones: yeah and i'll be checking with you and yeah. 317 00:44:22.830 --> 00:44:25.080 Dusty Jones: that's right yeah put the bad link up there. 318 00:44:26.520 --> 00:44:31.920 Dusty Jones: Thanks so much holly Lynn this has been great i'm i'm looking forward to listening to this again. 319 00:44:32.370 --> 00:44:32.820 Hollylynne Lee: yeah. 320 00:44:33.150 --> 00:44:34.350 Dusty Jones: Even though we're just doing this here. 321 00:44:35.400 --> 00:44:39.210 Dusty Jones: And to our listeners thanks again for listening to the teaching math teaching podcast. 322 00:44:39.630 --> 00:44:45.690 Dusty Jones: Be sure to subscribe to the podcast and we're hope you're able to implement something that you just heard you've had a whole list of things. 323 00:44:46.110 --> 00:44:53.940 Dusty Jones: and take an opportunity to interact with other math teacher educators, just like Halloween advised speaking of interacting. 324 00:44:54.420 --> 00:45:08.100 Dusty Jones: What do you want to hear about in upcoming podcasts and who do you want to hear from, let us know, through the virtual suggestion box it's on the contact us page at teaching math teaching podcast COM it's also in the show notes for this episode.