Humanizing Data

On-screen: [Center for MSIs. The Penn Graduate School of Education. Penn Center for Minority Serving Institutions: Elevate MSIs, Increase Scholarship, Connect Leadership, Inform Practices, Advance Policies, Enhance Capacity.]

Speaker: Andrés Castro Samayoa: Center for Minority Serving Institutions, University of Pennsylvania

Speaker: Andrés Castro Samayoa - Hello, everyone. Not only am I cutting the last presentation short, but then I’m also the person who’s standing in between lunch, so I’m going to try to make this lively and exciting. As Amanda so generously mentioned, I’m a humanist by training. I’m going try to talk a little bit about what it might mean for us to humanize data. As some of you might also know—I shared this with some folks yesterday and today—I’m from El Salvador. That’s home for me. It’s a little bit far away. Around December, I get a little nostalgic. I don’t get to go home too often. I usually get to go home around this time of the year. Over the time that I’ve spent time here in the U.S., when I go back home to see my mother and my brother, we sort of made a ritual and a tradition of (inaudible 0:01:52), turkey sandwiches. That’s basically what’s in season around this time of the year over there. They look something like this. They’re delicious. I encourage you to try them out.

I’d like to start off just by mentioning a little about this ritual that my family has of making the sauce that you currently see dousing that sandwich on the screen. Now, who in this room likes to cook? Good. Who has ever seen a cooking show? It should everyone in this room. (Laughter.) The process of making a sauce is perhaps not unfamiliar to you. You have a bunch of ingredients, you blend them up, and then you sift through them. In my family we’ve been using this one sieve for as long as I can remember. Actually, I’d like to use that image of the sieve to try to weave a narrative of how we might think about the production of research about ROI in education. Now, I thought I’d do a quick, little diagram of the complicated process of making a sauce. It’s not that complicated, but I think it’s a convenient analogy to how we might also think about producing knowledge, making research. We have this messy world out there that we try to apprehend in some way, shape, or form, through the way in which we think about the world, what I call our standpoint. Then, we make this nice, little thing that we like to call data. We’ve been talking so much about data. Then, we take it a little bit further, and then we apply some type of method. Through that, we create some type of knowledge.

I’d like to spend some time, or at least the time that I have here today, to think a little bit more about this idea of the standpoint. What is the way in which we orient ourselves to the world to try to make sense out of it? I think it actually has some implications to the way in which we talk about this notion of ROI, not only for MSIs but just more broadly as well. What I’ll venture to say is that the current snapshot or the prevailing wind about how we think about ROI, how we frame it for our prospective students and ourselves as researchers, is through something like this. Someone mentioned, just in this previous talk, the college scorecard. This is a snapshot of the college scorecard. It might not be legible. For example, in this little section here, you have this “Check out these schools, 25 four-year schools with low costs that lead to high incomes.” This might be the first point of entry for some of our prospective students to start thinking about what might matter in education.

What I would say is that it seems that there’s quite a bit of power in numbers when we think about education, when we think about why it is that we might want to become educated, or—and this is what I’m going to try to invite us to think about—is that we like to give a lot of power to numbers, not only in terms of how we talk about it with our students, but also how we talk about it amongst ourselves. Other folks have called it quantification of educational research. Now, I’m not new in making this claim, and I’m actually not here to try to offer a critique of quantitative approaches to educational research. This is a quote from (inaudible 0:05:06), but I do want to call attention to perhaps what might be some of the opportunities that we foreclose when we think about it from this particular point of view.

This is my proposition. I think that the quantified way in which we talk about ROI entrenches educational researchers’ empathy gap. What do I mean by that? What I’m actually talking about here is not perhaps how others perceive MSIs or how we might be conducting the research, but actually how we’re talking about MSIs and research with one another. Perhaps you’ve come across this idea of the empathy gap through the literature in psychology about implicit bias, but I’d like to extend that image to also talk about how we think about the research that we think is valuable and how researchers who are in quantitative fields might perhaps not be probably thinking about the historicity of some of the datasets that they’re using. (Inaudible 0:06:15) reading a little bit about this. I’ll let you go through this one, little quite that I think is particularly useful, and then I’ll try to illustrate what I mean with an example from the census.

We spent quite a bit of time thinking a little bit about data quality and also a little bit about what counts as data, but also how we make data count. What I think is important for us to take into account is that there is a risk in perhaps not necessarily historicizing how we think about data. I think that this little example illustrates that for us. These are the census race categories all the way from 1790 through the present. When we think about data, we also have to think about the embedded politics and racial ideologies in the datasets that we have amassed. There’s a willful desire to think about the transparency of that data when we’re producing these analyses. I think this goes back to one of Neil’s points earlier on, also one of the points earlier as well about the idea of data disaggregation as just becoming visible, and the instruments that we use are politicizing that they make us become visible.

Now, I’m not trying to make a wholesale dismissal of quantitative approaches to ROI, or as I recently learned, this one little idiom in English, throwing out the baby with the bathwater—such a peculiar image. (Laughter.) That’s not what I meant for here. It’s actually an invitation to think about the production of knowledge and production of research differently, with what I would like to call compassionate research practices, an opportunity for us to be producing quantitative research alongside those of us who might be humanist or who might be willing to properly historicize some of these datasets that we’re using for our research.

I started off with this image of sifting the blended mess that becomes into this delicious sauce. A couple of years ago, that one little cloth that we’ve been using in my family for as long as I can remember actually ripped. We didn’t stop using it. We ended up using a cheesecloth alongside that same sieve. What I’d like to think about is, how we as educational researchers are using different sieves alongside one another, and what ways can we actually come together to think not only about ROI in the ways we’ve been discussing here, but also alongside other (inaudible 0:08:55) might help us think about this in historicized ways as well. Thank you. (Applause.)

End of Humanizing Data video.

Video duration: 08:09