Importance of considering race in CS education research and discussion

I was talking with one of my colleagues here at Michigan about the fascinating recent journal article from Tim Weston and collaborators based on NCWIT Aspirations award applicants, which I blogged about here. I was telling him about the results — what correlated with women’s persistence in technology and computing, and what didn’t or was negatively correlated.

He said that he was dubious. I asked why. He said, “What about the Black girls?”

His argument that the NCWIT Aspirations awards tends to be white and tends to be in wealthy, privileged school districts. Would those correlations be the same if you looked at Black women, or Latina women?

I went back to the Weston et al. paper. They write:

Although all respondents were female, they were diverse in race and ethnicity. Because we know that there are differentiated experiences for students of color in secondary and post-secondary education in the US, and especially women of color, we wanted to make sure we captured any differences in outcomes in our analysis. To do so, we created a variable called Under-represented Minority in Computing (URMC) status that grouped students by race/ethnicity. URMC indicated persons from groups historically under-represented in computing–African-American, Hispanic, or Native American. White, Asian and students of two or more races were coded as “Majority” in this variable. Unfortunately, further disaggregation by specific race/ethnicity was not possible due to low numbers. Thus, even though the numbers in the respondent pool were not high enough to disaggregate by specific race/ethnicity, we could still identify trends by over-representation and under-representation.

18% of their population was tagged URMC. URMC was included as a variable in their analyses, and their results suggest that being in the URMC group did not influence persistence significantly. If I understand their regressions right, that doesn’t tell us if the correlations were different by race/ethnicity. URMC wasn’t a significant factor in the outcomes, but that is not the same as thinking that those other variables differ by race and ethnicity. Do Black females have a different relationship with video games or with community than white females, for example? Or with Latina students?

While the analysis did not leave race out of the analysis entirely, there was not enough diversity there to answer my colleague’s question. I do agree with the authors that we would expect differentiated experiences. If our analysis does not include race, can we account for the differentiated experiences?

It’s hard to include race in many of our post-secondary CS ed analyses simply because the number of non-white and non-Asian students is so small. We couldn’t say that Media Computation was successful with a diverse student body until University of Illinois Chicago published their results. Georgia Tech has few students from under-served groups in the CS classes we were studying.

There’s a real danger that we’re going to make strong claims about what works and doesn’t work in computer science based only on what works for students in the majority groups. We need to make sure that we include race in our CS education discussions, that we’re taking into account these differentiated experiences. If we don’t, we risk that any improvements or optimizations we make on the basis of these results will only work with the privileged students, or worse yet, may even exacerbate the differentiated experiences.

February 17, 2020 at 7:00 am 1 comment

Barbara Ericson's analysis of the 2019 Advanced Placement CS data

Barb spoke at CornellTech’s “To Code and Beyond” workshop on January 10 on her analysis of the Advanced Placement CS data (both A and CS Principles). She’s shared the slides and her analysis at her blog.

As usual, the analyses are fascinating and dismal. It’s amazing to see how few people are really getting access to AP CS.

This year, she did a bunch of intersectional analyses which were eye opening. Here’s a couple of the results that I found surprising. Only 9 states had more than 10 Black Women pass the AP CS A exam. Only 14 states had more than 10 Hispanic Women pass the AP CSA exam. Those aren’t percentages — that’s a raw number of exam-takers who passed. AP CSP numbers are larger, but still disappointing.

February 10, 2020 at 7:00 am 4 comments

Learning the Craft of CS Education: Recommending the CS-Ed Podcast

I’ve just finished listening to the first three episodes of the new podcast on CS Education from Dr. Kristin Stephens-Martinez (click here for the podcast homepage). If you teach computer science, I highly recommend it to you. It’s not about CS education per se, except in the sense that research often informs the education topics being discussed. Rather, it’s a nuts-and-bolts discussion of issues relevant to the craft of being a CS educator.

Kristin is terrific as the interviewer on the podcast. She plays all of us — teaching CS, looking for tips, trying to get what she can from these experts.

  • The first episode was with David Malan of CS50 at Harvard. It was an in-depth discussion of the tools they’re creating for CS50. I didn’t hear any that I was particularly interested in using, but I did hear about tools that I wanted to recommend to colleagues who teach those topics.
  • Dan Garcia of BJC fame at Berkeley was absolutely delightful. He didn’t talk about Snap! or Beauty and Joy in Computing. Rather, he gave a concrete checklist of how to develop good exams in CS. I’m a fan of checklists, and his were great. I’ll definitely use these tips in the future.
  • Amy Ko of U.Washington talked about her research, but in really concrete, practitioner-oriented terms. The first part was about how to help students debug. She definitely gave me insights on how to help students develop debugging skills. The second part was about how Donald Knuth was really a qualitative researcher — fascinating stuff.

I’m going to show up later in the series. I don’t know remember what I said! I hope that Kristin is kind to me in the post-production phase, because I don’t think I’m typically as grounded and offering concrete advise as these first three were. I’ll find out in the next few weeks.

TL;DR: If you teach CS, go listen to the CS-Ed podcast. You’ll get something useful out of it that’s worth your time.

February 3, 2020 at 7:00 am Leave a comment

Thorndike won. Dewey Lost: The Most Important 4 Words about the US Education System

One cannot understand the history of education in the United States during the twentieth century unless one realizes that Edward L. Thorndike won and John Dewey lost.
— Ellen Condliffe Lagemann

I mentioned that “Thorndike won and Dewey lost” on Twitter a couple months ago. I realized that some education researchers didn’t know this story. I first learned about it in Lagemann’s intriguing book, An Elusive Science.

Lagemann explains that Dewey was the pioneer at Chicago and Columbia, and recruited faculty and administrators that supported his perspective. But Thorndike came later and replaced those faculty.

Unlike Dewey, Thorndike favored the separation of philosophy and psychology. Despite considerable disdain for educators and an extremely imperialistic view of psychology, which he thought supreme for studying and controlling human affairs, Thorndike formulated ideas that were more suited to translation into formulas for educational practice. A conservative person whose prose was clear, to the point, humorless, and colorless, Thorndike was about as different from Dewey as two men could be. (p. 56-57)

Five years after Dewey left Chicago, Charles Hubbard Judd took his place. While Judd and Thorndike were rivals, they had similar views about the role and definition of school.

Over the years, Judd also recruited a faculty that was as supportive of his views as the Dewey group had been supportive of the views of their chief. (p. 68)

Although both thought experimentation was necessary in education, Dewey saw the school as the laboratory of education, whereas Judd saw the school as primarily the place for the implementation of real laboratory findings…Whereas Dewey saw teachers and researchers as more alike than different, wanting both to be skilled students of education, Judd believed that the improvement of education required the professionalization of education, which, in turn, necessitated tha teachers and researchers fulfill distinct roles. (69-70)

Audrey Watters has written a great blog post about the tension:

Ed-tech has always been more Thorndike than Dewey because education has been more Thorndike than Dewey. That means more instructivism than constructionism. That means more multiple choice tests than projects. That means more surveillance than justice.

If you do Web searches on “Thorndike won. Dewey lost,” you’ll find many relevant essays and papers. Dewey (wikipedia page) believed in educating the student, meeting them where they were, and helping them to develop in their community through teacher-driven innovations in the classroom. Thorndike (wikipedia page) was about administrative systems: grades, teacher requirements and credentialing, preparing students for vocations, testing (Thorndike is best known in psychology for his work on measurement), and teachers implementing what researchers invent. The US education system favors the latter.

I like David Labaree’s paper “How Dewey Lost: The Victory of David Snedden and Social Efficiency in the Reform of American Education” which summarizes why Thorndike won.

The pedagogically progressive vision of education — child-centered, inquiry based, and personally engaging — is a hothouse flower trying to survive in the stony environment of public education. It won’t thrive unless conditions are ideal, since, among other things, it requires committed, creative, energetic, and highly educated teachers, who are willing and able to construct education to order for students in the classroom; and it requires broad public and fiscal support for education as an investment in students rather than an investment in economic productivity.

But the administrative progressive vision of education — as a prudent investment in a socially efficient future — is a weed. It will grow almost anywhere.

When I look at computing education interventions, I see a lot informed by Dewey. Caring teachers, researchers working in partnership with practitioners (RPPs), and developers want students to engage and learn. That’s great, and as Audrey Watters has suggested, technology may be a way of making Dewey’s vision work in US classrooms today. But there’s likely a reason why Thorndike won.

It wasn’t luck. The US school system is built following Thorndike’s vision because his vision was more in concert with US values. I’m not an expert on how US values have driven the US education system, but I can guess at some of the factors. The US system is driven by the promise of compulsory education for all, a belief in rugged individualism, and the value for a capitalistic society.

  • We have a mission to educate everyone. When there’s a trade-off between increasing quality somewhere versus making sure that we can provide something for everybody, the most common choice is for the something for everybody.
  • We like our image of Americans as settlers/pioneers. No “hothouse flowers.” We’re “weeds” that can rise up to handle adversity. We want our education system to be small, minimalist, and local.
  • Education is expensive. States increase their investments only if (on paper at least) they can offer the same thing to everyone. The top goal in US education is to prepare workers, over a goal to prepare citizens. Our education decisions are dominated by economics.

Few students get access to computing education today (as I described in this blog post). The biggest barrier is that we’re too busy and resource-limited providing all the students the classes they need to meet current school requirements. See the principal in Miranda Parker’s dissertation who chooses to keep choir (which helps many students to get the credits they need to graduate) over CS (which only a few students might take). See the education faculty I talked about in my recent CACM blog, who are far too busy meeting state requirements for mathematics and science teachers to fit in CS which isn’t required to be taught pre-service. CS is something new that only a few students get excited about— that might be something Dewey would like since he values individuals finding their interests, but not Thorndike who values the education as a system for everybody.

The lesson is that if we want to get computing education in front of US students, we need to figure out how to make it work within Thorndike’s system. We have to be efficient. We have to do it with few resources. We have to fit into existing models. Alternatively, we can try to move the US education system into a more Dewey-like model — but we have to realize how big a shift that is. Thorndike won almost 100 years ago. The US education system has a century of ingrained views that align with Thorndike.

I wish I could argue for a more progressive view, but in the end: Thorndike won. Dewey lost.

January 27, 2020 at 7:00 am 2 comments

Is there a Geek Gene? Are CS grades bi-modal? Moving computing ed research forward

This month’s Communications of the ACM published Elizabeth Patitsas’s ICER paper about bimodality in CS grades (or rather, the lack thereof) as a research highlight, Evidence that Computer Science Grades are not Bimodal. It’s a big deal to have computing education in this position in the ACM flagship publication, and thanks to Shriram Krishnamurthi for his efforts in making this happen.

I wrote about Elizabeth’s paper when it was originally published at ICER at this blog post. Elizabeth wrote a guest blog post here on these topics (see here). These are important issues — Wired has just published an article talking about the Geek Gene with a great discussion of Betsy DiSalvo’s work (see post here about some of Betsy’s work).

I wrote the introductory page to the article (available here). I point out that Elizabeth’s article doesn’t end the debate, but it does move forward how we address questions about how we teach and how students learn:

This paper does not prove there is no Geek Gene. There may actually be bimodality in CS grades at some (or even many) institutions. What this paper does admirably is to use empirical methods to question some of our long-held (but possibly mistaken) beliefs about CS education. Through papers like these, we will learn to measure and improve computing education, by moving it from folk wisdom to evidence-based decision-making.

January 21, 2020 at 7:00 am 11 comments

Abstract Submissions Opens for FIE 2020!

Frontiers in Education 2020 conference will be in Uppsala, Sweden this year. Abstracts due Feb 2. Post below from Arnold Pears.

Featuring an all new submission site and new paper review system launched for FIE 2020.

Don’t delay! Secure your place at the 50th Anniversary FIE event by registering your Abstract today!

The 2020 programme features co-located workshops on Computational Thinking Skills for the 21st Century, the Launch Event for the IEEE/ACM Joint Curriculum project Computing Curricula 2020, a once in a lifetime conference banquet experience in the Uppsala Castle, and much much more.
Submit your Abstract NOW!

FIE 2020

January 18, 2020 at 7:00 am Leave a comment

Computing Education Lessons Learned from the 2010’s: What I Got Wrong

There’s a trend on Twitter over the last few weeks where people (especially the academics I follow) tweet about their accomplishments over the last 10 years. They write about the number of papers published, the number of PhD students graduated, and the amount of grant money they received. It’s a nice reflective activity which highlights many great things that have happened in the 2010’s.

I started this blog in June 2009, so most of it has been written in the 2010’s. The most interesting thing I find in looking back is what I got wrong. There were lots of things that I thought were true, ideas that I worked on, but I later realized were wrong. Since I use this blog as a thinking space, it’s a sign of learning that I now realize that some of that thinking was wrong. And for better or worse, here’s a permanent Internet record.

There are the easy ones — the ones I’ve been able to identify in blog posts as mistakes. There was the time I said Stanford was switching from Java to JavaScript. I should have fought for more CS in the K-12 CS Framework. And I should have been saying “multi-lingual” instead of “language independent” for years. And there was the blog post where I just listed the organizational mistakes I’d made.

The more interesting mistakes are the ones that are more subtle (at least to me), that took me years to figure out, and that maybe I’m still figuring out:

Creating pre-service CS teacher programs would be easy. I thought that we could create programs to develop more pre-service computer science teachers. We just needed the will to do it. You can find posts from me talking about this from 2010 and from 2015. I now realize that this is so hard that it’s unlikely to happen in most US states. My Blog@CACM post this month is about me getting schooled by a group of education faculty in December. We are much more likely to integrate CS into mathematics or science teacher programs than to have standalone CS teacher professional development — and even that will require an enormous effort.

CS for All is about Access. I used to think that the barrier to more students taking CS was getting CS classes into high schools. You can find me complaining about how there were too few high school CS classes in 2016. I really bought into the goal of CS10K (as I talked about in 2014). By 2018, I realized that there was a difference between access and participation. But now we have Miranda Parker’s dissertation and we know that the problem is much deeper than just having teachers and classes. Even if you have classes, you might not get students taking them, or it may just be more of the same kinds of students (as the Roehampton Report has shown us). Diverse participation is really hard.

Constructionism is the way to use computing in education. I grew up as a constructionist, both as a “technically precocious boy” and as a researcher. Seymour Papert wrote me a letter of recommendation when I graduated with my PhD. My post on constructionism is still one of the most-read. In 2011, I thought that the One Laptop Per Child project would work. I read Morgan Ames’ The Charisma Machine, and it’s pretty clear that it didn’t.

The idea of building as a way of learning makes sense. It’s at the heart of Janet Kolodner’s Learning by Design, Yasmin Kafai’s work, Scratch, and lots of other successful approaches. But if you read Seymour carefully, you’ll see that his vision is mostly about learning mathematics and code, through teaching yourself code. That only goes so far. It doesn’t include everyone, and at the worst implementations of his vision, it leaves out teachers.

I was in a design meeting once with Seymour, where he was arguing for making a new Logo implementation much more complicated. “Teachers will hate it!” several of us argued. “But some students will love it,” he countered. Seymour cared about the students who would seek out technical understanding, without (or in spite of) teachers, as he did.

Constructionism in the Mindstorms sense only works for a small percentage of students, which is what Ames’ story tells us. Some students do want to understand the computer soup-to-nuts, and that’s great, and it’s worthwhile making that work for as many students as possible. But I believe that it still won’t be many students. Students care about lots of other things (from business to design, from history to geography) that don’t easily map to a focus on code and mathematics. I still believe in the value of having students program for learning lots of different things, but I’m no longer convinced that the “hard fun” of Logo is the most useful or productive path for using the power of computing for learning. I am less interested in making things for just a few precocious students, especially if teachers hate it. I believe in making things with teachers.

The trick is to define Computational Thinking. Then there’s Computational Thinking. I thought that the problem was that we didn’t have a clear definition. If we had that, we could do studies in order to measure the value (if any) of CT. I blogged about definitions of it in 2011, in 2012, in 2016, and in 2019. I’ve written and lectured on Computational Thinking. The paper I wrote last Fall with Alan Kay, Cathie Norris, and Elliot Soloway may be the last that I will write on CT. I realized that CT is just not that interesting as a research topic (especially with no well-accepted definition) compared to the challenge of designing computation for better thinking. We can try to teach everyone about computational thinking, but that won’t get as far as improving the computing to help everyone’s thinking. Fix the environment, not the people.

But I could be wrong on that, too.

January 13, 2020 at 7:00 am 44 comments

Older Posts


Enter your email address to follow this blog and receive notifications of new posts by email.

Join 7,142 other followers

Feeds

Recent Posts

Blog Stats

  • 1,729,814 hits
February 2020
M T W T F S S
« Jan    
 12
3456789
10111213141516
17181920212223
242526272829  

CS Teaching Tips