Embodiment in CS Learning: How Space, Metaphor, Gesture, and Sketching Support Student Learning: Amber Solomon’s defense

Amber Solomon defends her dissertation today, co-advised by Betsy DiSalvo and me. I have learned a lot from Amber and her work. She came into her PhD studies with a particular perspective — a question about how we teach CS. She knew about the studies showing that spatial ability is correlated with success in computing. Why is that? Is it because there is something inherently spatial about computing? Or maybe because we are physical beings and come to understand everything in terms of our spatial experiences? Or maybe it’s because of how we teach computing?

That last one is concerning. Computing education is new. We haven’t spent enough time checking whether what we are doing is right for everyone — or if what we’re doing creates barriers for some students. In particular, she’s concerned about how we teach and learn with embodiment, i.e., references to space and our physical presence, in language, gesture, and sketching. In general, we don’t design our gestures and metaphors in CS education, maybe in part because Dijkstra warned us not to. That’s a problem. Because gesture has a cultural and social component, and we may inadvertently be teaching in a way that says to some students, “You don’t belong. We don’t use your gestures. We use ours.”

Amber’s first project was her study of our augmented-reality design studio for media computation where students’ work was displayed on the walls (see blog post here). One of the surprising outcomes in this project is that it influenced the climate in the classroom — students were more willing to seek help when everyone’s work was on display. The problem of a defensive climate in the classroom is longstanding in CS. Amber showed that changing the environment where we teach can change climate.

Amber with Miranda Parker led our SPARCS study, exploring why socioeconomic status (SES) predicts CS performance. In general, rich kids do better in CS than poor kids. Why? They compared two different models for why SES predicted performance on a standardized CS test. One model suggested that higher SES led to greater access to CS education. Rich kids got to take CS classes, camps, and robotics clubs while poorer kids did not. The second model suggested something more subtle — that higher SES predicted greater spatial ability which predicted better performance. That spatial ability model was a better fit to the data. Now consider Amber’s original hypothesis, that spatial ability predicts CS performance because of the way that we teach CS. The SPARCS study raises the possibility that the whole CS Ed system is rigged in favor of higher SES kids at a deep way. Just teaching more classes to lower SES kids won’t make a difference, if those classes are still taught in a way that requires higher spatial ability.

Amber’s dissertation asks two big questions: (1) How do teachers use embodiment when they teach CS? (2) How do student use embodiment when they learn CS? Part of the answer to the first question appeared at ICLS last year. I talked about helping with Amber’s coding of student videos in my blog post about Dijkstra. Her summary is below.

I’m not going to summarize her whole dissertation here. Here is one example from her defense. She shows a video clip of a teacher explaining a function call. He points to a function definition and says, “Now we come here. I am five. N is five…Do you see what I’m doing?” Read that last sentence imagining that you’ve not had years of CS or mathematics teachers modeling this kind of language. Who are “we” and what does it mean to “come here”? What does he mean that he’s five? Now N is five? Is he N? When he’s saying ‘what I’m doing,’ what is he referring to? Playing the computer, or writing the program, or drawing on the slide? Now imagine hearing that and you have visual disabilities and don’t know that he’s pointing at a function definition. Amber supports a strong claim in her dissertation — we have not designed the language and metaphors of CS education. There’s no way that we CS teachers plan to say things which are that confusing.

Throughout her PhD career, Amber has written about her experience of being a Black woman in CS. She taught me what intersectionality is about. I am grateful that she has been both a CS education researcher and activist during her PhD. I am grateful to have had the chance to work with her.

Title: Embodiment in Computer Science Learning: How Space, Metaphor, Gesture, and Sketching Support Student Learning

Amber Solomon

Human-Centered Computing Ph.D. Candidate

School of Interactive Computing

College of Computing

Georgia Institute of Technology

Summary:

Recently, correlational studies have found that psychometrically assessed spatial skills may be influential in learning computer science (CS). Correlation does not necessarily mean causation; these correlations could be due to several reasons unrelated to spatial skills. Nonetheless, the results are intriguing when considering how students learn to program and what supports their learning. However, it’s hard to explain these results. There is not an obvious match between the logic for computer programming and the logic for thinking spatially. CS is not imagistic or visual in the same way as other STEM disciplines since students can’t see bits or loops. Spatial abilities and STEM performance are highly correlated, but that makes sense because STEM is a highly visual space. In this thesis, I used qualitative methods to document how space influences and appears in CS learning. My work is naturalistic and inductive, as little is known about how space influences and appears CS learning. I draw on constructivist, situative, and distributed learning theories to frame my investigation of space in CS learning. I investigated CS learning through two avenues. The first is as a sense-making, problem-solving activity, and the second is as a meaning-making and social process between teachers and students. In some ways, I was inspired to understand what was actually happening in these classrooms and how students are actually learning and what supports that learning. While looking for space, I discovered the surprising role embodiment and metaphor played while students make sense of computation and teachers express computational ideas. The implication is that people make meaning from their body-based, lived experiences and not just through their minds, even in a discipline such as computing, which is virtual in nature. For example, teachers use the following spatial language when describing a code trace: “then, it goes up here before going back down to the if-statement.” The code is not actually going anywhere, but metaphor and embodiment are used to explain the abstract concept. This dissertation makes three main contributions to computing education research. First, I conducted some of the first studies on embodiment and space in CS learning. Second, I present a conceptual framework for the kinds of embodiment in CS learning. Lastly, I present evidence on the importance of metaphor for learning CS.

Date: Monday, April 12th, 2021

Time: 2:00pm – 5:00pm (EDT)

Location: Bluejeans Link

Meeting URL

https://bluejeans.com/182730963?src=joininfo

Committee:

• Dr. Betsy DiSalvo (Advisor, School of Interactive Computing, Georgia Institute of Technology)
• Dr. Mark Guzdial (Advisor, Electrical Engineering and Computer Science, University of Michigan)
• Dr. Ashok Goel (School of Interactive Computing, Georgia Institute of Technology)
• Dr. Wendy Newstetter (School of Interactive Computing, Georgia Institute of Technology)
• Dr. Ben Shapiro (College of Education and Human Development, Georgia State University)
• Dr. David Uttal (School of Education and Social Policy, Northwestern University)