S2xE1: Supporting Students of Color with Manuel Pérez-Quiñones

January 4, 2021
S2xE1: Supporting Students of Color with Manuel Pérez-Quiñones

In this episode, we talk with Manuel Pérez-Quiñones, a Professor at the University of North Carolina at Charlotte. Our topic is supporting students of color. We discussed why this support matters and the differences between professors versus students and equality versus equity. We also discussed how he changed his grading practices for his remote class. He even shared about changing his syllabus to specification grading, which he reflects on in his blog now that the semester is over. For his too long; didn’t listen summary, Manuel talked about how we need to acknowledge the history that got us here and what is happening right now and then consider the repercussions that appear in our classroom.

You can also download this episode directly.


Kristin [00:00]: Hello and welcome to the CS-Ed Podcast, a podcast where we talk about teaching computer science with computer science educators. For context, we are recording this episode on September 21st, 2020. So, potentially, some of the things we talk about will feel dated by the time you listen to this. May the future, though, when this podcast is released, be better than our present. With the disruption of Covid-19 and the latest calls for a change in education due to racial inequality. This season’s theme is “Where should we go from here?” in hopes you can all take a pause and ask ourselves, “If I had time to reflect rather than react, what should I be doing?” I am your host, Kristin Stephens-Martinez, an Assistant Professor of the Practice at Duke University. And joining me today is Manuel Pérez-Quiñones, a Professor at the Department of Software and Information Systems at University North Carolina at Charlotte. So he’s just down the road from Duke University, though we are recording in our respective closets today. So Manuel, tell us about yourself. How are you doing?

Manuel [00:58]: I’m doing okay. It’s kind of odd to be recording inside of a closet, but I’m doing fine, thank you. I’m a Professor in the Software Information Systems at UNC, Charlotte. I’ve been here… this is my sixth year. Before that I was at Virginia Tech for 15 years and before that for four years at the University of Puerto Rico-Mayagüez. I teach mostly… I teach in the two extremes of the CS curriculum. I like to teach intro courses, either intro or media comp or data structures. And then I’ve taught a lot of HCI senior level or graduate courses in HCI. And I have, probably the reason why you asked me here, I have a lot of experience running programs for diversity. At one point, I was associate dean of the Graduate School of Virginia Tech in charge of an Office of Diversity Initiatives. And then I’ve also was involved in a lot of CS broadening participation activities. So the coolest—and an interesting fact—the coolest job I’ve ever had, I was Visiting Professor at the U.S. Naval Academy, and the students in there, when you walk into the classroom, they all stand to attention. It’s kind of cool. And then they just stay standing and you say, “You may be seated.” And then the class commences. So it was kind of odd. I had fun, though. It was a fun experience.

Kristin [02:19]: Oh, that’s good. So as you hinted in your introduction, our topic is supporting students of color and we’re going to have a moment of preaching to the crowd where the first question I want you to talk about is how is supporting students of color not disadvantaging white students? Because I feel that a lot of people, when they first hear about this idea of supporting students of color or whatever name you want to use they often then think of it in terms of, “Oh, so you’re going to make it worse off for these students that don’t fall in that category.” So how is that not disadvantaging the students that don’t fall in the category of the students you want to support?

Manuel [02:55]: Yeah, that’s a good caveat, because I think you’re absolutely right. People automatically assume two things. One is you’re taking away from the others. Or you’re giving something for free to the students of color. So I’m gonna give you a couple of things. One is supporting one group does not mean ignoring the other. But the reality is that we’re not supporting students of color just because of the color of their skin or something like that. We’re supporting students of color because of sort of systemic discrimination that they face.

So let me, let me sort of enumerate these things. We’re supporting students who come from a high school that was underfunded. If you come from a high school that was underfunded, there were fewer classes available, no AP courses and things of that sort. We’re supporting students who come, who don’t see themselves in the profession, right? There’s few professors that looked like them. We’re supporting students who are so few in your program that networking and mentoring and even just emotional support is not there because there’s only two of them in the whole program. We’re supporting students that, by nature of their socioeconomic, they have to work part time or they have two jobs or they have home responsibilities. They’re the ones that pick up their siblings from school and things of that sort. We’re supporting students who face explicit discrimination and aggressions whether it is physical, microaggression, et cetera. And we’re supporting students from different cultural models. There is a, there’s a theory that talks about different models of culture, collectivistic versus individualistic and power distance and things like that.

The reality is, all those things that I told you, unfortunately, are proxies for students of color. The two go hand-in-hand, and that’s why I mentioned that it’s because of systemic discrimination. And we’re only doing this not because we want to give them anything free. We’re doing this to level the playing field. We’re doing this because they have fewer opportunities to succeed given the history before they come into the classroom, than other students. So, if you’re white and you’re in some of those categories, you’ll benefit from what we have to do to support you. But the reality is that the majority of students that faced those conditions that I mention are students of color.

Kristin [05:25]: So I think the gut reaction to a response like that actually would be something along the lines of like, well, then why don’t we actually find the students that fit all of those categories and not use the students of color as a proxy.

Manuel [05:37]: Because we’re pretending that color doesn’t have a hand in this right? The reason why all of these students that are disadvantaged are disadvantaged is because they live in neighborhoods of color, because they face discrimination because of that. But when they come to the classroom, I guess… I guess the reason why I make the caveat is when they come to the classroom, I don’t want professors to think, “Oh, you’re Black or you’re Latino, therefore you’re not X.” And I want them to think, “Oh, you’re Black or Latino. You might have been coming from a background that puts you at a disadvantage.” I don’t want them to think that there is something intrinsic in their biology that says, “Oh you’re just not as good as the others.” That’s sort of the caveat. But yet you’re, you’re absolutely right. I mean, there is a lot of universities that are talking about increasing support for first generation students. And again, that’s highly correlated with Black and Latinos. But it’s not necessarily the case. And depending… we’re close enough to Appalachia and that area has a lot of low socioeconomic first generation white students. They face a lot of similar issues.

There is a—I don’t know if this is related to that or not—but there is a similar thing that I’ve commented. I mean a lot of professors look at students from the view of when they were students. “When I was a student, I used to do this.” The way I’ve started explaining it to professors is, “You are an NBA player. You have no business telling a 17 year old how to dunk a ball. Cause you’re at the top of the top. You’re at the professional leagues. You’re in a professional team. If you’re really good, you might even be an all star NBA player. You can’t expect them to do the way you did it. You can’t. It’s just not, it’s not a fair comparison. So you got to get off your high horse and think, what would it help you to do it this way? Rather than say, “When I was a student…” Yeah. No, no, no. You’re not the model.

I mean, we only question support for students of color, but we don’t question it when we’re talking about gender. And to me, that is a sign that we’ve moved forward. I’m sure 20 years ago we were questioning supporting women in a different way. So we have CRA-W, NCWIT, Anita Borg, you know, all these organizations that are providing support for women and it’s okay. We don’t fight.

And I mean, there is still a little bit of pushback. But I would like us to get to the point where we have a similar level of support at the high level for all the other students that are disenfranchised in computing without say, “Well, how is that fair?” I mean, there are still people that say that when we have, we run programs for women in my college. And I’ve heard people complaining, “When are you doing that for me?” But it’s not as prevalent as it is when we’re talking with students of color. We need to level the playing field that, that’s the way we should think about it. And because of systemic discrimination in society, some groups come in with less preparation to succeed. Doesn’t mean they’re not capable. It just means that they haven’t had the same opportunities.

Kristin [08:59]: All right. I think that leads to maybe finishing out that thought of why is this so important?

Manuel [09:05]: Yeah, I mean, that’s at the heart of it, right, is the difference between equality and equity, right? I think we make the mistake too often of saying, well, I treat everybody the same way. But everybody’s not the same. I mean. It’s thinking that because I give the same assignments to everybody on the same due date and I grade them all, you know, blindly not knowing who submitted it and all that: everything is equal for everybody. It isn’t, if they came with, you know, a lack of preparation because of no fault of their own. It has nothing to do with intellectual ability. It has nothing to do with effort, intention. It has to do with the fact that my high school had no CS class or my high school had only up to algebra or, you know, whatever. So you don’t have the background and we can’t—if we want to broaden participation—we can’t just assume that, before they show up at our door, everything is equal because we know it is not. So we have to sort of find a way to provide that scaffolding to make it a little bit more fair. You know, it’s the difference between equality and equity.

The other thing is, is I mean, there is a lot of, there’s a lot of privilege that happens in a lot of all these things, right? When I was at Virginia Tech, we used to have a lot of students that came from a really fancy school in Northern Virginia. They had four years of computer science in the high school. Four years. Needless to say, those kids showed up and they, I mean, they would engage me in conversations in the freshman year that I wasn’t comfortable having, you know? This is like, yeah, last time I looked at that was when I was in undergrad. That was 30 years ago. I don’t want to talk about that now. They were that strong, prepared, lots of experience. I, you know, I felt bad for them because sometimes they would be a little bit bored and I would engage them in conversation, “Well, do you want to try this other thing?” to sort of keep them engaged. But there’s no way I could teach my class thinking everybody was like them. And we had, I don’t know, 10 to 15 from that high school every year. So it was a significant mass. It wasn’t one kid that was good. It was like a large number. And you compare that to the kid that came from a, you know, poor neighborhood that never saw a computer science class before. And they’re in the same class. And that’s not, that’s not equal. That’s not fair. So we, I mean, that’s why it’s important. We have to find, we have to find a way to do that.

The other reason is that computing is a really lucrative career. So, I mean, if we are to close the economic inequality and to improve social mobility and all these things that we talk about. Computing careers is one way of doing that. And, you know, if we’re keeping people out for the same reasons that keep inequality in society, economic inequality, you know, we’re part of the problem. I mean, we literally are part of the problem. You know, it’s not us computer science professors, it’s not our fault that high schools are underfunded and are in the problems that they face. But if we let that be a reason for us not to bring other people in. We’re just passing the problem to somewhere else.

The last reason which, before the pandemic and before the social unrest, usually was the first reason I used to give. But now it’s less. The last reason is we’re building products. I mean, the computing field is building products for other people to use. And if we don’t bring in the diverse population that reflects society, we’re building products that is not going to take into consideration things. There was a tweet yesterday of somebody saying that one of these online platforms with virtual backgrounds doesn’t work as well if you’re Black.

Kristin [13:22]: I saw that Twitter feed, Twitter thread.

Manuel [13:24]: You know, it’s like. Oh, my God. You mean to tell me that all the time that it probably took to develop that algorithm, you never, not once put a Black face in front of it. I mean, it’s the rest of the world that bland and white that you’ve never tested this with one person. And you get to the point of releasing the product and then you’re like, “Oh, crap, look at that.” I mean, that’s pretty scary, you know? And it’s not the first time this has happened. This has happened before with some of the online photo things that auto classify people. And this keeps happening.

And we’re seeing it now with Covid. Right. We’re seeing how Covid is attacking Black and Latino communities harder. And we automatically just think, “Well, it’s because they’re poor, because this, or because the other.” But not thinking that maybe in medicine we’re not quite where we should be on understanding symptoms, that there might be differences. I don’t know if there are or not. And there is a good rationale for this because, you know, we—a lot of—I mean, we do this in computer science education too. A lot of the experiments we run, we run them in our classrooms or in our universities. And again, the population there is not reflective of the whole society. So we’re missing details in how we run, how we do science sometimes. You know if there isn’t a good argument from the sort of social justice and equity point of view, there’s certainly a good argument for building better products for society.

Kristin [15:06]: Yeah. That’s definitely one thing that frustrates me about products, because like another example are things like products that are geared towards women. And then you look at it, you’re like, “I don’t think there was a woman on the team when they developed this thing.”

Manuel [15:22]: Here’s another part of that—this connects several conversations we’ve had already. I mean, a lot of times when we’re talking about women in computing, the one or two or three or four females in our departments that are professors, I often tell them, like, “You know, you survived this mess. You are not normal. You’re above average. You’re like, there’s a reason why you’re still here, even though the system is stacked against you. So, I’m glad we’ll use you as a role model. But let’s find out what we need to support the other ones because you’re a champion, you’re better than most of the men here, because that’s why you survived in it.” Sometimes we say, “Well, we talk to the women in the department.” It’s like, yeah, those are superstars.

Kristin [16:13]: None of the professors are a representative sample of anyone.

Manuel [16:15]: No, and much less though—so as I mean—I get asked so many times about Latinos and immigration. And I go like, “Okay, I’ve never immigrated. I’m Puerto Rican. I was born a U.S. citizen. So I have nothing to say about that. I know I’m Hispanic. I know I’m Latino, but I can’t comment on that. I mean, I can only comment on that just as anybody else that reads the news.” And sometimes we just use the few of us, as you know, “Talk to your people and tell me what your people think.” I don’t call Latinos at night. You know, it’s nonsense. But we only have one or two in our departments. And in our social circles sometimes we got none. So that’s it. You know, the only Black person I know is a professor down the hallway. That person is more like me than all the other Blacks in the society because he’s a professor, he’s got a Ph.D., he’s in computer science. There’s probably more in common among us in a computer science department than us with our own circles, you know.

Kristin [17:15]: Yeah.

Manuel [17:15]: So, yeah, I mean, we need to do better.

Kristin [17:19]: So with that in mind, how do we do better? Like, how do we do better is a very broad question. Let’s go at it with a… first the bigger picture of the mindset that we should have when we approach this problem rather than going into the nitty gritty…

Manuel [17:38]: Yeah. And that’s one that I’m, in the last—I don’t know—four or five years I’ve been changing a lot how I think of it. I think, you know, just—again—just because of the training that we’ve gone through, I used to think of, you know, strict deadlines and, you know, I give everybody the same opportunity. And it sort of realized that’s part of this fixed mindset. That is not growth mindset. I mean, there are things that our students don’t have that put them at a disadvantage. And I have to think about how I give them an opportunity for them to grow, rather than give them an opportunity that because of their background, they’re already disadvantaged. And I’m not opening the opportunities for them. So I think I think a lot of things about…

So I’m testing this crazy thing out this semester. I’m doing what’s called specification grading, and I sort of explored it a little bit. And it makes sense for me to try it given my class is completely online, given Covid, given the social unrest, given all these things, I thought, “This makes sense for me right now.” I don’t know if I would have tried it a year ago. I don’t even know if I would do it in person. But the idea of specification grading is that I’m going to set up all my assignments have satisfactory or unsatisfactory.

Kristin [19:19]: OK.

Manuel [19:20]: And satisfactory means you’ve done an honest effort to get it. It’s not A, B, C, or D, so it’s probably lower than an A, fairly lower. And it also means that I’m going to give students multiple attempts in trying to get it to satisfactory. So I’m removing this idea that, you know, by Friday at 5:00 when this is due, if you don’t get it, you don’t get to get it ever. No, I’m going to give you a deadline so that you know you have to work on it at a certain time. But I’m going to give you a week or two to get it. And because we’re online and because I’m using a lot of autograders, makes sense. I can just say, “This homework is due on Friday. You got an extra week to get it in. You get multiple attempts. If you get stuck, call me. I’m here. I have office hours. I’ll walk you through the problem.” And each assignment has multiple problems and each problem you can submit individually multiple times. So they have plenty of times to sort of get to a point where it crosses a satisfactory bar without worrying about, oh my God, I’m going to get an F because I didn’t hit 15 percent or whatever.

So and then at the end of the semester, your grade is determined by how many of these you completed. Not by the score. How many of these did you get to a satisfactory level? So there is an incentive of counting how many assignments I get to satisfactory. Not a punishment or a reward about my actual score on each assignment. So you could, I mean, my drill and practice activities, the satisfactory bar is 80 percent. You get to 80 percent. You’re satisfactory. You’re good. And you need to do eight of those. And I think I have 10 in the semester.

And then I also have programming assignments. I also have exams and all that. So the idea is, you know, I can from the get go, I can work really hard at multiple times. I don’t have a fixed deadline. I can take a little extra to each of them, get to 80 percent. And if I’m, if I’m driven, I’ll try to get to 100. But it doesn’t matter. Once you get to 80 percent, it’s satisfactory. You’ve done enough.

So there is a lot of literature around grading for equity. And this is an idea that sort of came out of that community. And I really like it. I’m going to—I’m curious to see what it will happen at the end of the semester. What I suspect will happen, and this is my hypothesis here, is that the A students will get an A no matter what. But the students that were on the lower end of passing, you know, the C’s and the D’s would go up. So I suspect that the number of C’s and D’s will decrease and the number of B’s will increase. That’s what I’m expecting to happen.

So, but I mean, I think the idea is that you need to go into the classroom, trying to encourage a growth mentality in the students. Trying to encourage the students to work a little harder and move forward rather than say, “Well, if you don’t get it, you don’t get it. I don’t have time to help you.” And I think we do that way too much. And I mean, there’s been a lot of people who have said because of the increasing enrollment, that they’re worried that this is what it will translate to. That we’ll just get really, really picky in the grading and say, “I’ve got a hundred and fifty. I can do good with 80 and the rest… they’re on their own.”

Kristin [22:56]: So let’s see if I understand this correctly. You have ten sets of problems. And the students can hammer at these problems all semester even if they wanted to. And their goal basically is to pass 80 percent of the problems in each problem set.

Manuel [23:16]: Yes. So I have drill practice problems which are, you know, “Write a function that counts how many even numbers are in an array.” That type of level right? And each one of those assignments has five to eight problems. And I have ten of those assignments. They need to get eight of those 10 to get an A in the class. If they do—I think it’s—seven they get a B. They do six they get a C. If they do, I don’t remember, five they’re in the D. And now for the A in the class, they have to get that. They also have three programming assignments and they got to do 80 percent on all the programming assignments. They have two midterms and a final. The final is optional, and if you take the final, it will replace one of the two midterms. For the A, you got to get a 90 percent average on the exams.

And there is another assignment that I do in my class, I call it a “tech note.” And it’s sort of like writing a readme for a technology. And they have to write like a two page, you know, “This is what’s cool about this technology,” and then they have to give a five minute presentation and there’s two of those in the semester. But the video, they only have to do one of the two to get an A.

So the A’s define, they call them a bundle. The A is a bundle of, “You got to do eight of the workouts. You’ve got to do all three programming assignments. You got to do 90 average on the exams. You got two out of three shots at it. And you got to do all the tech note assignments.” The B, it’s a fewer counts on the bundle. So instead of eight assignments, you do seven and so forth. Each of them is still at the satisfactory level across. It’s just fewer number.

So in a way, you’re encouraging more practice because you still have to get to 80 percent. But you’re also acknowledging that if you don’t do as many, then you got a lower grade. One of the advantages of this approach is that it’s easier to grade because you don’t have to grade to take points. You have to grade just to see if it is satisfactory or not. And if it’s not, you highlight what needs to be improved and then they submit again. So you reduce grading because of all the assignments that look fine, they look fine. And all the ones that look just horrible, they’re not satisfactory. And you send them back. So you’re not grading for nitpicking, you know, “Two points minus this two points minus that,” which is where a lot of time takes.

Kristin [25:55]: So this, this very much reminds me of the book “Grading for Equity” by Joe Feldman, which is going to be a different episode of the season. And it sounds like you’ve adapted a bunch of the practices that he talks about in his book. And one of them is such like you’re trying to make the letter grades actually mean the student has an A level of mastery or B level of mastery. And rather than using the kind of flawed mathematics of, “You have X number of points and there are Y possible, so X divided by Y, that’s what you get as your grade.”

Manuel [26:30]: Right.

Kristin [26:30]: You’re disconnecting that by having these criteria, like if you want an A level, you need to do these particular things. If you want a B level, you need these particular things that kind of demonstrate that you’ve only mastered a B level of this material.

Manuel [26:44]: And it encourages them to multiple attempts. Right. And it removes this idea. I mean, I’ve always been frustrated with deadlines, as a professor, because it doesn’t matter what deadline I put for an assignment. Invariably, I get busy with something and then I don’t grade it for two days. And I always feel guilty, like, “Why didn’t you ask them to turn this in by 5:00 p.m. on Monday if I wasn’t going to look at it till Thursday?” You know, it’s like, why, why I make them be busy and then I just sit back and do nothing. That always bugged me and this removes that because the deadline is irrelevant. It’s like if, you know, you can submit it and keep working on it and submit it again.

Kristin [27:25]: Yeah. My gut reaction to your, to your statement is that, well, you want to provide students with a reliable cadence to the class so they don’t feel stressed by wondering when is the due date for this thing?

Manuel [27:41]: Oh, absolutely. By the way, I have due dates for everything. And there is—I think, I’ve set them up to be like a week later. After that point it’s late. So the only reason why I have due dates is for them not to try to do them all the week before the final. There is no late penalty. It’s all you know, you got to work on this a little bit every day to survive the semester. You can’t just put it off for two weeks and then come back. Hence the deadlines and the due dates.

The other aspect of the due date is, for example, the first programing assignment that I’m about to throw at them later this week. Well, you should have done the workouts that came before because that knowledge sort of it’s related. So the due date is sort of trying to connect those things in account, particularly for an online class where, you know, there is no meeting. It’s completely asynchronous.

Kristin [28:37]: So do you explain that method behind the madness to the students?

Manuel [28:41]: I have a, like, half hour video of the first day where I come to class going over that, yes.

Kristin [28:47]: Would you be willing to make that kind of video available to anyone?

Manuel [28:52]: I guess so. I didn’t record it thinking of it being broadly available… What I have basically is, is it’s me walking over the portion of the syllabus that explains the grading and just sort of, you know, going over the write up and marking it—I think I was marking it with a pen—and just talking, saying, “If you do this, if you do that, you get an A.” I have a little table that shows it, I could probably make that available, yeah. Absolutely.

Kristin [29:29]: Because I think generally it would be useful, I think, for more of us to share the method behind the madness of our syllabus.

Manuel [29:37]: Oh, absolutely.

Kristin [29:38]: So once it’s after the due date and it’s late, but there’s no late penalty could they potentially like, this first one, they don’t get to 80 percent, but then they move on and then halfway through the semester they’re like, “Oh, I think I actually could get an A in this class.” So they go back and get to 80 percent on that thing. Can they still do that?

Manuel [29:58]: I, I… When is this podcast going to come out?

Kristin [30:03]: It doesn’t come out until next semester so don’t worry.

Manuel [30:05]: Okay, so, so sorry students, which… I’m willing to discuss it, but the reality is that the workouts: there are ten and they need to do eight to get an A. So even if you have one that doesn’t get 80 percent, you still got two more you could throw away. So there should be less of a, “I want to go back and redo the first one to see if I get it to 80 percent.” And there should be more of, “If I keep practicing regularly, I should be hitting 80 percent in all of them.” And there shouldn’t be a reason because you can drop two.

So I’m hoping that that won’t be the case. But I’ve always told students this: “Look. I don’t mind if you woke up halfway through the semester and became very responsible and did fantastically the rest of the semester. I don’t mind going back and saying, ‘Yeah, you weren’t here the beginning… ’” I’ll give you a break on something because it demonstrates that they’re doing better. I have little sympathy for the one that is pulling a 60 on everything throughout the semester and shows up saying, “Can I get an extra assignment?” No, on the regular assignments, you haven’t done any honest effort. Why would I give you extra? But if their score’s going up, if they sort of caught on late or life threw them a curve at the beginning of the semester and they’re doing better at the end, I have no problem accommodating because, you know, life happens outside of the classroom.

Kristin [31:33]: Alright, I want to be careful of time. What suggestions do you have for something we can do right now? What we can try and accomplish in a year? And what we should try to be accomplishing in five years?

Manuel [31:43]: So right now. Number one: grading. I think we need to do grading in a way that supports growth mentality. That means rewrites, more flexible deadlines, all the things that we just talked about. I think we pretend too much that students have nothing else happening in the world but my class. And they have all the time in the world to devote to my class.

And that’s just not the reality, particularly for students of color. I mean, again, if you hear what we said at the beginning of what the high correlation is between the communities of color, low socioeconomic, multiple jobs, et cetera, et cetera. So that’s first.

The second thing I would say is we need to increase student support. I think we’ve accepted that we need student orgs for women, but we haven’t quite accepted that we need student orgs for students of color, minorities. And I mean, computing is actually late on that. I mean, I think there is a lot of, you know, NSBE for African-Americans in engineering, SHPE for Hispanics in engineering, and we don’t quite have the equivalent. There’s a lot of efforts and things in place. So consider: Be more flexible in tutoring hours. A lot of places are doing this with women. They hire the upper level women to be tutors and mentors to the younger women in the program. Why aren’t we doing that with Black and Latinos, too? I mean, a lot of these things, we need to increase the support, particularly for the students of color. And the good news is that we know how to do this because we’ve been doing it for women for a few years.

But the bad news is that we’re somehow gun-shy if we do that for Black or Latinos. It’s like, “You did it for gender. Why can you do it for race and ethnicity? “

The other one, and I wish I had a solution to this one, is we need to make faculty be more comfortable talking about these issues. Our faculty in computing—and probably not the case for computer science education like you mentioned at the beginning—but in general, a lot of them are really, just, really uncomfortable addressing these issues.

Kristin [34:06]: That discomfort. I think we all need to get past that. The only way to get past it is to do it.

Manuel [34:12]: Yeah.

Kristin [34:14]: What about from a year from now?

Manuel [34:17]: One thing that I’ve been saying a lot lately is that we need to use data. We talk about data driven decisions and data this data that data the other. And I think we’re hiding some things with data. I think. I think we’re using data in the wrong way. I think, we look—I mean—I’ve heard this so many times in running diversity activities, “Oh make sure you collect data so we know if that one works, we’ll repeat it next year.” I’m going, “No, I mean, I can collect data on an event to see if it works correctly. But it’s not going to solve diversity or discrimination on one event.” It’s not, that’s not how it works.

So you have to use data to identify problems, not data to congratulate yourself. I think we need to use data. We need to do more climate surveys. We need to identify what is it that is blocking our students from succeeding rather than saying, “I had a seminar. People liked it.” The data shows that. That does nothing because it doesn’t break down the problems you have. So, I mean, I think we need to start collecting data to identify problems, not to congratulate ourselves that we’re doing the right thing. It’s too easy to ask, “Did you like that? Yeah, that was good.” Out of politeness, you say that.

But the problem is still there, you know. And I think, I mean, this is very typical to debugging, right? You don’t do debugging to prove that the problem is correct. You do debugging to identify the problem. Just because you couldn’t catch the problem, that doesn’t mean the program is correct. But you still got to do the testing to try to find the problem. That’s the way we should be using data. Let’s find where the discrimination happens in your department, in your classes, in your clubs, and go after those problems and fix it. And that takes a while to start collecting that data, to start thinking of data that way.

The other thing that I think—and I don’t know that this is a year—but within a year, we should start thinking of this alternative path. A lot of people are saying that the idea of a pipeline is the wrong metaphor. That instead of saying you start, you know, in middle school and then at the other end you become a professor and along the way we lose people. It’s wrong because what if in middle school you never heard of a computer? But in high school you got excited about it. The pipeline mentality sort of loses that and I think you need to think of alternative pathways instead of pipeline, meaning, “I went to a high school. I didn’t have calculus, but I really like computers. Can I study computer science? Well, that’s going to be really hard because you’re going to have to take this math and this other thing.” And that’s just, that’s a roadblock. That’s assuming that before you get to college, you were already in the pipeline to come to college. Or people that get a degree in whatever discipline and then couldn’t find a job and then go, “Oh, look, there’s lots of jobs in computer science. Let me go study computer science now.” That’s a person that, coming into computer science, it’s gonna struggle. There’s got to be a way that you can say, “I just want computer science lite because I have an accounting background.” I mean, this is an idea of CS + X that we’ve been talking about. We need to think of what those alternative paths are. And we’re doing more of that, I think. Data science is forcing us to do that because data science has so much of other things that they look like a CS-lite in some way.

They’re not a CS degree, but they’re very tightly connected to computer science. Right.

So I think we’re starting to consider that. I think we need to do more of that, of having those alternatives. And we can’t put them in place in a year. But we need to start thinking deeply about what that is. So that, you know, your next question is, in future years, we can start creating more programs that consider those things.

Kristin [38:37]: Yeah. All right. So let’s go with our last part TL;DL: too long didn’t listen. What would you say is the most important thing you’d want our listeners to get out of our conversation?

Manuel [38:49]: So, let’s see if I can summarize this. When it comes to students of color, I think you cannot ignore their history. I think you cannot ignore the history of their background, their schooling, their communities. I don’t think you can ignore the history that they’ve had in this country. I don’t think you can ignore what’s happening on the streets today. So the first thing is, you’ve got to consider that their success in the classroom is not just about intellectual ability, but it’s a bunch of other other things that are normally an uphill battle for them. And you need to find a way to say, yeah, those two things are tightly connected. So I know that’s sort of really difficult for a lot of people, particularly in computing. I don’t think we think of social—well, that’s not fair. People in HCI like me or in education do think about these things.

But the other thing is, I think, we need to come to terms with the fact that computer science is very, very interdisciplinary. It’s not the old computer science where the heart of it was theoretical computation. I think there are a lot of parts of the field that have to do with other things more applied. And somehow those areas tend to attract more people or more diverse people. I still don’t quite understand why that’s the case. And as a matter of fact, every time I say it I cringe. But I think we need to think that computing is much more bigger than what we think it was. And that means change the courses, change the approaches, change the type of degrees.

And the last one is… And this is so obvious that I don’t know if it needs to be said. I see CS education as a collaborative effort. Education is not about us deciding what we teach. It’s not about a curriculum that ACM gave us. “That’s what needs to be in this class.” It’s a collaboration with the learner. I mean in education they have this set of theories of learner-centered pedagogy. I think we need to embrace that. We need to say, “This is a collaboration between what I want to teach, what the student wants to learn—or what the student doesn’t know that needs to learn—and the curriculum somehow.” And somewhere, if you really want to make a difference, you’ve got to take all three into consideration. We can’t assume, “Well, you have to have…”—I mean, I pick on calculus all the time because it’s the one that our students sometimes struggle with. You can’t just wash your hands and say, “Well, if you can’t pass calculus, you can’t be a computer scientist.” You know that that’s not exactly 100 percent accurate. Maybe you can’t have some types of jobs in computer science, but you could be a web developer without calculus. And that collaborative effort is where we would define new paths through a CS curriculum that will probably help make up the problems from high school or the problems from outside as they interact with our curriculum.

Kristin [42:18]: Yeah. Alright. Well, thank you so much for joining us, Manuel.

Manuel [42:24]: Happy to do it. This was fun.

Kristin [42:26]: And this was the CS-Ed Podcast hosted by me, Kristin Stephens-Martinez at Duke University, and produced by Amarachi Anakaraonye. And, remember, teaching computer science is more than just knowing computer science. And I hope you found something useful for your teaching today.


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