Cat: 0:00

When we started dating, you told me that I was like someone who grew up in the 1800s.

Ashley: 0:04

I mean, there's literally a photo of you that is in black and white poking a sheep with a stick.

Cat: 0:15

We had a lot of thoughts and feelings about what technical work in the world looks like, what the future of that work is, how do we understand it, how do we make change about who gets to be technical.

Ashley: 0:28

This is a podcast about what it's going to take to train and include the next generation in technical fields.

Cat: 0:34

As, the resident wife guy in this situation, I feel I'm uniquely qualified to introduce Dr. Ashley Jauvinett. Ashley is a neuroscientist. You're an educator. some might call you an education activist, actually, you're a rabble rousing educator, changing the game for students in neuroscience. You're an associate teaching professor at UC San Diego. You're also a musician, is something that was really important to me when we first met because I fell in love with you when you were playing an Ellie Goulding cover. We were both in grad school.

Ashley: 1:09

Gosh, that feels really comprehensive. I'm also your wife. I feel like that needs to go on my list of accolades.

Cat: 1:13

So you, a neuroscientist have to introduce the psychologist. Now,

Ashley: 1:17

I'm super excited to introduce Dr. Catherine Hicks. Who is a social and data scientist and really like a force for the science of developers, like someone who is leading the psychology of software teams. She is the creator of the developer thriving framework, which has really been, I think, one of the first really, really good examples of someone who is studying software teams with evidence based data. And finally, also someone who's an advocate for open science someone who believes that from start to finish, the kinds of science we do should be accessible, reproducible, and shared with everybody who can benefit from it.

Cat: 1:56

I think your journey into coding has been really, really interesting and it's not always one that people hear. So can you tell me like a little bit about what got you to being an assistant teaching professor?

Ashley: 2:08

I don't have any computer science training. And this is the thing I tell my students in my programming class. On the first day of class is like, I never took a programming class,

Cat: 2:18

But you're teaching them.

Ashley: 2:19

But I'm teaching them how to code, right. And how to think about code. And so for me, I am a neuroscientist, as you said, and I started my coding journey when I was a graduate student and I was handed a bunch of data, stacks of images and told to analyze it. so I had to learn and I got like a MATLAB for neuroscientists book and did some of the exercises, but it wasn't super useful. Like the thing that was actually useful was just having my own data and having to play with it and edit other people's code.

Cat: 2:49

This is just really plunging me back because we met in grad school. I was a psychology graduate student I had absolutely no idea what neuroscientists did at all. Like, I thought, okay, somebody's there studying the brain, that's fine. And then I met you and I was like, what's happening? You're in the, this dark lab, you're working with lasers, there are viruses involved. Like, just tell the people a little bit, what did you do in neuroscience? Where does this data come from? Like, what is it like?

Ashley: 3:18

Oh man.

Cat: 3:19

what's MATLAB? Because I don't think people know that.

Ashley: 3:21

Yeah, neuroscientists, we want to understand how the brain works. If you want to understand how the brain works, you can study humans, but you can only do so much in humans. You know, you can put humans in a fMRI scanner, get some big pictures of their brain, but it doesn't tell you about what neurons are doing. And if you want to explain how the brain works, you usually want to know what neurons are doing. So, we turn to animal models, things like mice and sometimes monkeys or zebrafish. And we do fancy things in those animal models to target specific sets of neurons. So when I was in grad school, I was recording lots of images where I had tagged specific sets of neurons to try to understand what they were doing in the brain. And we were doing this in a mouse model and the lasers comes in because you shoot lasers at the brain to target these proteins that change how bright they are based on how active the cells are.

Cat: 4:14

I remember feeling like you had access to these superpower things that I had like never even touched at the time in grad school. I hadn't even like touched a microscope because I didn't really, really, cause I grew up homeschooled. And then in college, I didn't take any laboratory classes. My college didn't even have those open to people who were not like STEM majors.

Ashley: 4:33

Yeah.

Cat: 4:34

I met you and you were like, Woo lasers. One day it's a laser. One day it's a virus. You know, like you were learning like a million different skills a day. I felt like, did you, do you feel like you came in like confident about that?

Ashley: 4:46

I started graduate school with enough research experience to get me in the door, but a pretty narrow slice of research experience. I had gone to a small liberal arts school where I didn't take enough math and I didn't take enough programming, and then I was thrown into this, like, very competitive, very top notch PhD program, and I had to learn a lot on the spot. And no, I felt like I knew nothing, and I was learning everything.

Cat: 5:14

What happened after you were like, I got to teach myself to code.

Ashley: 5:20

think I spent most of my time in grad school wrangling MATLAB, which by the way is a programming language that is mostly used by engineers, a little bit in academia. It's, uh, it's okay. It's pretty, it's pretty good for some things.

Cat: 5:33

No MATLAB slander is going to occur here.

Ashley: 5:35

It's not open source and we're a big fan of open source in this room. You work

Cat: 5:38

in python now, right?

Ashley: 5:39

I do because when I started my job and I started realizing that I went on this journey of learning how to code and I felt like You know, other people, too, are going to be on this journey, you know, the students that I'm working with, and I wanted to give them the tools a little more up front and a little more, you know, in a way where everybody has access to them, um, and feels like they can get those skills. And so for me, It didn't feel right to teach MATLAB. It's not the most ubiquitous programming language, especially outside of academia. I decided, okay, I'm going to learn Python. And when I was in like the first two years of my job, pre tenure started learning

Cat: 6:20

You actually a professor about to teach it, and you were like, I better learn this so I can teach it, right?

Ashley: 6:27

Yeah. And I'm not the only person who has like done this, right? I'm one of many, many people who either for research or teaching or whatever else, right? Has had to teach themselves these skills on the job.

Cat: 6:38

I've done research with all these software developers. Thousands of developers have come through my studies at this point. And there's always, like, this moment. Almost on every topic I've ever studied because I study psychology and it brings out like the deep stuff for people and almost every study that I do, there's a moment where people start to say, um, I bet you've never heard this from anybody before.

Ashley: 7:03

Hmm.

Cat: 7:06

Hmm. I wasn't trained like everybody else. I'm kind of an imposter. I got to tell you, it's really radicalized me to have like thousands of people tell me that they're the only person like this. And it's, I would venture to say self teaching is kind of like the majority out there.

Ashley: 7:23

I think that's a hopeful thing because it's, we, we need those people. And, you know, there's limited space in every introductory computer science class and every bootcamp. Like if people aren't teaching themselves, we're behind.

Cat: 7:35

So you taught yourself to code. tell me about that classroom, because I think there's some stuff that's really interesting here. Because you're teaching computing, you're teaching coding from outside of computer science.

Ashley: 7:48

Hmm.

Cat: 7:48

like?

Ashley: 7:50

Yeah, I think I had to do quite a bit of soul searching and also speaking to colleagues about, you know, like, what is it that people actually need to know? You know, I'm not training back end software engineers. I'm training people who are going to go out into the world, want to do something with their data or build a computational model of some sort. And they don't need to know every single in and out of, like, object oriented programming or something. I landed on a few principles, which is like, one, we should try to teach as little syntax as possible and try to teach as little like memorization as possible. And that's even more true now in the age of AI assistance. And two, we need to have it be like much more data focused than, you know, a typical computer science class. So a little bit more of a data science y sort of feel to it. And. So that's like the content of the class, right? But that's only half of it. The other half of it is how you teach the content and how do you convince biology students, one, that it's worthwhile to do this and two, that they can do it because most of them walk into the room thinking, wow, gosh, I'm not a hacker. I'm not a math person. They have all of these preconceived notions about what it even means to learn coding, right?

Cat: 9:01

Stereotypes.

Ashley: 9:02

They have stereotypes and that stuff gets in the way of everything else. Like if you don't talk about that stuff, if you are not acknowledging that that's in the room with you, you can't learn how to code. just can't.

Cat: 9:15

So, why should those students go learn programming in a biology classroom, not in a computer science classroom? I know you have thoughts about this.

Ashley: 9:23

People should learn computer science in a biology context because We've thought about what they need to learn, and it's more tailored to what they need, I think, in the end. And two, there's like a big identity piece of it. So I just co wrote a paper with a student who took one of my classes who did sort of venture into the computer science and engineering side of campus to get access to some of these skills. And she told me, you know, even in an introductory computer science class, there were people, many, many people in the room for whom that wasn't their first time, right? So they a

Cat: 9:58

lie.

Ashley: 9:58

it's a, it's a lie.

Cat: 9:59

intro, CS class is actually an intro CS class on a major university campus anymore.

Ashley: 10:05

Totally. And we have different tracks on our campus, but still, right, there are the students who would prefer to take a class that's like a little bit below their skill level, so they come into the room with the knowledge, right? And so, one, like, she really felt that. And two, I mean, to be frank, if you walk into a computer science classroom on my campus, it is predominantly male. And she really, really felt that. And. Like my class looks dramatically different than that. And we can do everything we want to like, try to make women feel included in these spaces. But look at the end of the day, if you're looking around and it's like mostly dudes, and it's mostly people who aren't in your major also, who you would never see outside of this one class, you know, it's just, it's alienating. Creating a space for those students where they feel like they can learn amongst peers, truly peers who feel like they're in the same boat. That's really meaningful.

Cat: 10:56

Yeah. So there's like this concept in psychology that you know pretty well called sense of belonging.

Ashley: 11:01

Hmm.

Cat: 11:02

Mm we are constantly looking around our environment, scanning our environment, and we're asking, do I belong here? Do I belong here? Even, even if they say I belong here, do I really belong here? Right? Like we're smart about this. This is survival

Ashley: 11:16

hmm.

Cat: 11:17

in a deep way. This is about what you think is possible. I've measured this with software developers, even, even with highly male dominated fields. This still matters deeply to people. So it matters for folks who have an identity that's not represented. It also matters, it does damage if you don't have sense of belonging for everybody. And, you know, in our research, we've seen like for professional software teams, the ones that say I'm on a team where I really do feel like we've committed to this value. They report being more productive and they say my team is more effective. Like a very real outcome in the world.

Ashley: 12:02

that was my entry into computing, right? Like, what is yours?

Cat: 12:05

You've had years of hearing me say that I don't want to freaking teach myself to code. So

Ashley: 12:12

And feeling like you need, feeling like you need some, some level of something to call yourself, you know, data scientist or whatever.

Cat: 12:18

Oh man, design, and I, I discovered that I loved that stuff. I really like logic puzzles, and I really like, like, long-form fiction, like, I like novels, I like storytelling, and I had never thought those things would make me good at math. Never. Which I think is, by the way, just like, in general, if you are good at books, you might be good at math. Like, computers are there to do a lot of math for us, so we can do this, like, narrative logic stuff when we're working professionally with math. So I started doing statistics professionally, and that was like a huge, beautiful, game changing moment where I was like, Oh, I actually am good at this. I'm smart at this. I'm actually pretty kick ass at this. Heh heh. But it hadn't translated to coding for me for a long time. And I think it was really just stereotypes again I grew up really like, not with a lot of exposure to computing. In fact, um, you know, my parents would like rarely if ever let us use the computer, they thought the computer was really dangerous. I grew up kind of feeling like, this is not for me, whatever is happening here, like sneak time on computers at the library,

Ashley: 13:42

where you had to like sign up and like get a specific computer. I remember those days. Yeah.

Cat: 13:47

they're not letting you just sit around and like open, I mean, I remember the first time I saw like a little terminal window and I was like, it's the most terrifying thing I've ever seen ever. I didn't need to walk into a classroom and see it, that it was all men to feel like me, You know, a poor little queer girl, like, you know, I, I was not welcome here. I co founded a tech startup and I worked with the most lovely People on earth, my co founder chap Snowden, our chief engineer, Kirk Collins. And they were both just like cat. This psychology stuff rocks. It's so good. They were like, it's telling us what to build. It's adding so much value. And I remember this day that chap was like, you want to sit down and push some code and go through that process and have someone review your, that code. We can do that was like the first time I felt like someone looked at me and said, what do you mean you couldn't be a developer? You could be a developer.

Ashley: 14:42

We've talked sort of about like the asymmetry of these things like It's so unusual for someone to look at someone with the skill set that you had at the time, which was, you know, predominantly in psychological, experimental research, and social science, and say, yeah, no problem, become a developer, right? But we do the opposite all the time. We say like, you know, yeah, sure, you know how to code? Like, whatever, play with X, Y, and Z data, play with, you could, you could tackle whatever discipline you want after, you know, coding. Like, we have this asymmetry.

Cat: 15:10

I started consulting. I was like, I can't afford a statistics software anymore. Now I have to learn R. And do you remember I was like, had taken on a research contract and I was like, I'm going to teach myself how to process this data in R and it's going to require coding. And I was sitting on the couch and I like looked at you and I said, is this what coding is?

Ashley: 15:33

Those were your exact words. Like, this is it. Like just typing some words into this, like, you know, text console. You're like, really? Like, this is, this is the like enigmatic thing we've been like skirting around for so many years that I've been convinced that I like, wasn't able to do.

Cat: 15:52

Yeah. And it's so funny because now I see those things and I feel like fondness. I see like a big messy terminal window or, or I see like, you know, even things I don't understand. And I'm just like, I love this stuff. I love working with developers. I love thinking about how people work in code. Yeah.

Ashley: 16:10

Yeah. And here's the thing. Like you came into coding and into data science with. an understanding of how experimental design works and also where it goes wrong and like that sort of really deep technical logical understanding of how we collect data when we know there are real differences like that we use statistics to back up at its core is really right like logic you came in with that and I feel like that has put you and your work you Way above anybody else who can whatever like throw a bunch of data into some fancy Statistical pipeline without actually understanding why they're making those choices and you know those things.

Cat: 16:58

thank you. It's not just about me. Like, like, think about your students. Like there was a student of yours who actually went to Microsoft, right? Who's like, had a biology background.

Ashley: 17:08

This student of mine She Was a neuroscience major She took one of my classes where we did some coding and kept learning after class and graduated, went to Microsoft, but not in like a quote unquote technical role, like in some sort of consumer facing role. And eventually like launched herself through a lot of self advocacy into a role where she's now a software engineer. Like, proper, you know, recognized as such with that skill set. And, um, I think, you know, you and I are in some, in some cases in some sort of flavor like that because we are people too who have learned these skill sets and have had to advocate for like, okay, no, no, I am someone who can teach this class or no, no, like I am someone who can do this data analysis, you know, and run this experiment and do this research. And, um, there's more of us. There's so many more of us out in the world.

Cat: 18:06

Yeah, right. And like, you're missing out,

Ashley: 18:11

Yeah.

Cat: 18:11

Like if you don't have us, if you don't make it, honestly make it easy for us actually to get into this stuff, right?

Ashley: 18:18

Yeah. You're missing out on all the people who have all of the ideas and that like diversity of thinking from other fields outside of computer science and engineering.

Cat: 18:28

How do biologists think about code that's different?

Ashley: 18:32

Well, I think I wouldn't say we think about code differently necessarily. I mean, code is just the means to an end, right? But we think about data differently. I mean, The kind of data we collect is often really noisy, and you don't know where that noise comes from. You don't know if it's from the thing you're using to measure. You don't know if someone bumped the microscope or the animal at that time. You don't know if it's real biological noise, right? And so we come in with skepticism about data, about where it came from and where the noise is from.

Cat: 19:05

Do you feel like people in tech don't come in with enough skepticism about data? Yeah.

Ashley: 19:13

of the stuff we've talked about, like in our conversations about like messiness, right? Like what data is not there?

Cat: 19:19

This is a scientist's way of thinking about signals.

Ashley: 19:22

yes. yes.

Cat: 19:23

are like, Oh my God, I found something. It's definitely real. I'm going to believe it forever

Ashley: 19:27

Yeah, totally. And

Cat: 19:28

to raise a million billion dollars.

Ashley: 19:31

totally. And you've, and you've all the data you've played with in your training as say, like a data scientist is, you know, the Iris set on Python, which is Perfect. It's a perfect data set. There's nothing wrong with it or the Titanic data set or any of these other toy data sets.

Cat: 19:47

in a bad way?

Ashley: 19:48

Well, it's, it's, it's just like a complete data set. There's no, you know, like noise that is not intentional, right? And we play with that data. It's nice and tidy and, you know, great. Yeah. I can run dimensionality reduction on that. Cool. Um, but hand me another data set. I can't assume that it's clean in the same way. And that's the kind of lens I come in with as a biologist that I think a data scientist doesn't always have.

Cat: 20:14

I have a background in learning science too, and how people learn, and one thing that I think is so cool about the discipline based computing that you do is like, you know, people, need all kinds of things to learn and they need like abstract things and they need general principles and they need to put it into practice and they need applied situations. And I think that halo effect really bothers me. The idea that everybody should just go to a CS department and that they'll learn these things about how code works. And that doesn't match up with how students actually are like making decisions.

Ashley: 20:50

Oh, totally. Yeah. Yeah, totally. And I think something that's come out of my research, which I didn't expect, the short term. Can I just do something with this bit of code is like just as rewarding as the long term thought of like, maybe someday this will get me a job or allow me to do X, Y, and Z with my data. Like the sort of short term, like, can I do like a fun, weird personal project around this is like super gratifying. Or like, I do work in a research lab. Is there something I can do with the data I have now that like actually, you know, could, could be the thing that motivates me to take this class and to learn these skills? And I think a lot of like, movements around trying to make programming more inclusive have focused on the long term. Well, like you're going to make money, like you're going to make a ton of money if you get this, more jobs, you know, it's a good skill set to have. And like, that's nice, but like, you know, at the end of the day, you get that like little burst of dopamine just to throw some neuroscience in it,

Cat: 21:49

Yeah,

Ashley: 21:49

right? You don't get dopamine from like thinking about your retirement savings. You get dopamine from like the stuff you can do today.

Cat: 21:57

totally. Well, okay, there was this thing. So I used to work in schools and on like these all these like long term education research projects. Right. And I always had this thing in my mind, which was like do only the rich kids get to have fun or what? Like,

Ashley: 22:09

Mm,

Cat: 22:11

is that is that not something we all need? Like all these inclusion programs can be so somber.

Ashley: 22:20

Oh, totally,

Cat: 22:21

so like, You know, I on the goal. And I mean the, the lot, look, it's important to tell people there are these careers. We want you to know about it. We want you to know, but I think you make a really good point. We cut those people out from. experimentation. There's some great work from I think Matt Ruffalo about how the same technology program went out to different schools, and the same like resources, like same little devices for kids, same curricula, but teachers in the wealthier school had the kids explore and do self directed. You know, and, and, right, like, remember me with the computers? When I got my first computer in college, I was like, I better not break this computer.

Ashley: 23:05

Mm hmm, Mm hmm,

Cat: 23:07

one thing that I have to do is not break the most expensive thing I've ever owned.

Ashley: 23:11

Mm hmm, Yeah,

Cat: 23:12

that is not a situation in which you're going to be coding a bunch.

Ashley: 23:17

no, and you know, like, so in this big survey I ask of my class before and after the quarter starts, um, I ask them one item among many others. This one item has changed the most out of any other item on the survey, and it is exactly what you just said. The item is, I worry that mistakes I make will damage my computer. And students start the quarter, and they're like, yeah, I really worry about that. And then you think about your low income students. You think about the students who just bought the first laptop they'll ever, you know, have ever owned. And they are deeply concerned, as you were, about literally breaking the computer. How do you learn coding when you can't play?

Cat: 23:59

Hmm. So what do you do to help them feel like they can?

Ashley: 24:05

I break my computer in front of them a lot. I mean, and I really feel like that's, that's it, right? Like I walk into the room and I say, I've never taken a coding class. I'm gonna make mistakes in front of you. You're gonna ask questions. I don't understand. I'm gonna sit here and generate a bunch of errors and I actually, I have this activity, which I love. Um, on like the, you know, in the first week where I have them intentionally generate multiple kinds of errors because it's, you got to immediately get over the fact of like, okay, got an error. It didn't, it didn't break anything. Right. It's fine.

Cat: 24:42

You're like, this is actually the assignment. I bet they have a lot of fun with that.

Ashley: 24:46

Totally. Because so much of like learning how to code is interpreting those errors. Right. And so, all right, let's get them all. Let's see what they, see what they mean and then grow from there. Yeah.

Cat: 24:57

That's so cool.

Ashley: 25:02

And someday I probably will accidentally, like, hack into my computer in the wrong way, and But we haven't done that yet. Yeah. Yeah. The ultimate learning moment.

Cat: 25:19

Open science is like huge in neuroscience. Like it's really important. I don't, I don't know if people know that, like people who aren't neuroscientists have any idea of that.

Ashley: 25:28

I recently gave this like very silly nerd night talk about how anybody can be a neuroscientist, which is just based on the premise of like, look, there's so much neuroscience data online. If you know a little bit of programming, you could get into it and start taking a look at it. Um, so that's one side of it, but, but neuroscience. Yeah. And I think like a lot of different fields of science in general are really into open source tools and sharing. You know, not, not remaking the wheel from the beginning, sharing tools and things like that. Yeah, and I know you've, you've tried to like get this going in your work, right, with your research So back to that, like what, what is it that you are trying to open up in your world?

Cat: 26:11

Yeah. I lead a team that does social science research about software developers. And. That is a small world. Like, I mean, I'm looking all the time for other psychologists who are working with software developers or software teams, and, um, there are not a lot of us. Um, and so what I'm trying to do with my research is bring evidence about what helps people innovate and learn and thrive together, and I think that evidence has to be shared. Like as two scientists, we both feel like the only way we all move forward is with like generosity from the get go, like share all the evidence and then everybody will benefit and everybody will flourish. So the research that I lead is like shared out in the open and also the methods that we use. Cause it's hard to measure like big human things. And I don't think software development always knows how to ask questions that come from psychology. Um, those things matter to me a lot. And then it matters because. What I found about this was so that people can trust and replicate my research, like the more that I share, the more that someone else can pick up the baton and build on it. And how cool is that? Because I certainly don't have all the answers. And so that's like a huge value, you know, of ours. And I think it really aligns with like the software developers who say, if we're going to build a world that relies on all this technology, We have to know what it is. Because we have to be able to like triage it if it breaks, you know, and share the load and, and figure out, you know, how to kind of approach this almost like a shared infrastructure that we're all relying on. I kind of think of scientific evidence that way.

Ashley: 27:54

I love that. I love that.

Cat: 27:55

from the people. So it's like from the people to the people, like the data in our research belongs to the people who did it. So

Ashley: 28:03

absolutely. Absolutely. And so, I mean, for the, for the folks who don't know how this might work when it's not open science, like how is this different than a typical sort of publishing process in

Cat: 28:16

Oh yeah, of course. Okay. So this is a great question. Cause I did not know this when I went to a science grad program, I was like, I am going to, I got into this PhD. I guess I will. Um, I, I've thought that science was just like a thing that, you know, I had that was out there. Um, uh, I was sorely mistaken. So there's this huge system where academic scientists publish in academic journals. And those journals are not always open. In fact, the fight to get them to be open to the general public has been like a long term, you know, fight. And typically you access them like through your university or university might have a subscription to them. Um, Um, and it's kind of everything that you get judged by if you're in academia, is having these publications. That is, it's, it's, people have this idea of like a beautiful ivory tower, life of the mind, which I think you and I have really fought for, right?

Ashley: 29:21

Mm

Cat: 29:22

it's very output driven in a lot of the time and that's a huge mistake. I don't think science used to be that way, but. I actually think some of the stuff you've done in neuroscience challenges that because you've been part of these like many lab collaborations that are like less competitive,

Ashley: 29:38

mm-Hmm.

Cat: 29:38

yeah, in a nutshell, that's how scientific work goes out in these journal articles. And then it's like not very accessible to the general public.

Ashley: 29:46

You started in this environment where it was like, not only are computers like not for your gender, right, but also they're just like this totally foreign thing of the outside world and we don't like the outside world in our house. It's how I understand your upbringing. So, so this is like the starting point for you and here you are, you know, someone who is technical, is working directly with software engineers. Like, how? Yeah. How did you overcome this? Is there still a feeling of, is this your world? Like, what is, I don't know, what's your sense of belonging on the Kinsey scale? Like what?

Cat: 30:27

When we started dating, you told me that I was like someone who grew up in the 1800s.

Ashley: 30:33

I mean, there's literally a photo of you that is in black and white and you are literally poking a sheep with a stick.

Cat: 30:39

In a long denim skirt, long hair. It's really cute.

Ashley: 30:45

It's maybe like my favorite photo that has ever existed of you ever. And it looks like it is from the 1800s.

Cat: 30:51

Yes. So I was raised in a very conservative religious community and we had sheep, which was a highlight of my life. Love them, miss them, um, would herd them around this big, big property that we had. I was good at all kinds of things. I was really good at repairing electric fences and I was really good at canning peaches. I took a lot of pride in those things and I was really good at reading books. We would go to the library and the librarians would just be like, here's these kids again at 11 a. m. on a Wednesday for some reason, they're checking out a hundred books.

Ashley: 31:31

Yeah, did you like max out the number of books you were allowed to check out at any given time?

Cat: 31:35

They didn't have a limit.

Ashley: 31:37

Oh, wow. Oh, wow.

Cat: 31:39

was my physical strength So I was raised with a lot of big beliefs that college was not the best place for women. Um, uh, so before we even get to technology, it was like, could you even be in a classroom? I went to college anyway. And that was really, really scary for me. And I remember I got to my first college class. It was a 9 a. m. Spanish class. Um, and I got there an hour ahead of time because I was so worried I was going to do something wrong because I didn't know how you're supposed to be in a classroom.

Ashley: 32:17

Yeah. Yeah. First time?

Cat: 32:19

I was so excited to get homework for the first time. I was like, I've heard about this. I'm gonna kill this.

Ashley: 32:25

Didn't you write, like, you had, like, an, uh, a, like, semester long essay assignment and you, like, wrote it in the first

Cat: 32:31

I wrote all my, if I knew what an end of semester assignment was, I did it in the first week of class. I took my very first statistics course in grad school, which was taught by Mark Applebaum who, um, rest in peace, Mark, one of my beloved faculty professors in grad school. I was sitting there in the classroom and he was writing and it, this was a tough class. Like this had math PhDs in it. It was like almost a hazing ritual. Like you're either going to get through the first year stats class or you're not. And it was assumed that you had a whole lot of background that I definitely did not have. And he wrote a bunch of equations out and I didn't know the Greek symbols for anything. I didn't know like the, you know, the symbol for sum and all of that. He had written out this big long thing and I was sitting there in the classroom, Like just with this mounting almost like this buzzing in my head like oh my god. It was so such a huge deal that I even got into a PhD program I had to move, you know I could barely I couldn't afford a car at the time and then I was like I'm gonna fail in this classroom right here right Now like this is it And then he was like, any questions? You know, this moment after doing all this like equation work? And I like, was like, fine. If I'm gonna fail anyway, I might as well be like, I've lost. So I raised my hand and I was, I was like, um, and he's like, okay, like, what question do you have, you know, about the statistical thing? And I'm like, no, no, no, Dr. Applebaum, like, I don't know what this symbol means. and He was like, Oh, okay, like which one? And I was like, honestly, all the way back to like the top left hand corner of the board. Every single one after the first one, um, I'm having a problem with. This is why he was a great teacher. I'm tearing up remembering this. He looked out into the classroom and he said, uh, anybody else? And several other people raised their hand. That, that changed my life, he never made fun of me. And he was like, I better adjust this class a little bit. yeah I actually rocked it. I got an A in the, the second, uh, course in that series. And then I did a PhD dissertation on disclosure in classrooms.

Ashley: 34:50

You had so much to lose by putting yourself out there because of how much work it had taken to get to that moment. Like, what do you think was going through your head

Cat: 35:04

I think I'm willing to be really embarrassed for the things that I love I, I think I'm willing to fall on my face I think I deserve to be there even with all the barriers and all the doubt and all the like, I couldn't afford a computer. I had a laptop that had been my mom's old laptop that was about to come apart, and I still believed that I deserved to be there. And I've just always had that.

Ashley: 35:31

Mmm. You had this conviction that you We're here for a reason, because you had worked hard, because you deserved it. Like just that idea, you know, because I think not only do we need to see this in ourselves so we can advocate for ourselves, but we also need to see this in our students, in our colleagues, in our managers, in the people that are around us.

Cat: 35:54

There are some wicked problems in the world, like really hard things going on, and cutting ourselves off from like all the diverse ways of thinking and problem solving is the silliest, it's the stupidest thing we could possibly do.

Ashley: 36:08

Yeah, especially if it comes down to just like, not knowing what a Greek symbol means, right? Like, like that's, like, that's such a low level, like, silly, you know, thing, like, okay, like, once you know that, great, fine, you know, like, you can do the logic to figure out the math, that's fine. At the end of the day, like, it's a low level problem to have

Cat: 36:29

it's syntax.

Ashley: 36:31

If that's what we're using the gatekeep, like then we're, we're just gatekeeping the kids out who aren't like, you know, willing to ask

Cat: 36:37

there's like this logical fallacy that you and I talk about a lot, which is like, people think that something being rigorous is just small numbers, like, a small percentage of people get through this thing. I mean, like, a small percentage of people will survive getting hit on the head. Like, not how we should select software developers. You do all this work, which is like, how do we make teaching better? What if a teacher is really good, and then all their students succeed? Should we blame a really good teacher for having high success in their classroom? It's like, ludicrous.