Posts tagged ‘spatial reasoning’

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)

April 12, 2021 at 9:00 am 4 comments

How do teachers teach recursion with embodiment, and why won’t students trace their programs: ICLS 2020 Preview

This coming week was supposed to be the International Conference of the Learning Sciences (ICLS) 2020 in Nashville (see conference website here). But like most conferences during the pandemic, the face-to-face meeting was cancelled (see announcement here). The on-line sessions are being announced on the ICLS2020 Twitter feed here.

I’m excited that two of my students had papers accepted at ICLS 2020. I haven’t published at ICLS since 2010. It’s nice to get back involved in the learning sciences community. Here’s a preview of their papers.

How do teachers teach recursion with embodiment

I’ve written here about Amber Solomon’s work on studying the role of space and embodiment in CS learning. This is an interesting question. We live in a physical world and think in terms of physical things, and we have to use that to understand the virtual, mostly invisible, mostly non-embodied world of computing. At ICER 2018, she used a taxonomy of gestures used in science learning to analyze the gestures she saw in a high school computer science classroom (see link here). Last summer at ITiCSE, she published a paper on how making CS visible in the classroom (through gesture and augmented reality) may reduce defensive climate (see link here). In her dissertation, she’s studying how teachers teach recursion and how learners learn recursion, with a focus on spatial symbol systems.

Her paper at ICLS 2020 is the first of these studies: Embodied Representations in Computing Education: How Gesture,Embodied Language, and Tool Use Support Teaching Recursion. She watched hours of video of teachers teaching recursion, and did a deep dive on two of them.

I’m fascinated by Amber’s findings. Looking at what teachers say and gesture about recursion from the perspective of physical embodiment, I’m amazed that students ever learn computer science. There are so many metaphors and assumptions that we make. One of the teachers says, when explaining a recursive function:

“Then it says “… “now I have to call.”

Let’s think about this from the perspective of the physical world (which is where we all start when trying to understand computing):

  • What does it mean for a function to “say” something?
  • The function “says” things, but I “call”? Who is the agent in this explanation, the function or me? It’s really the computer with the agency, but that doesn’t get referenced at all.
  • Recursion is typically explained as a function calling itself. We typically “call” something that is physically distant from us. If a function is re-invoking itself, why does it have to “call” as if at a distance?

For most computer scientists, this may seem like explaining that the sky is blue or that gravel exists. It’s obvious what all of this means, isn’t it? It is to us, but we had to learn it. Maybe not everyone does. Remember how very few students take or succeed at computer science (for example, see this blog post), and what enormously high failure and drop-out rates we have in CS. Maybe only the students who pick up on these metaphors are the ones succeeding?

Why won’t students trace their programs?

Katie Cunningham’s first publication as a PhD student was her replication and extension of the Leeds Working group study, showing that students who trace program code successfully line-by-line are able to answer more accurately questions about the code (see blog post here). But one of her surprising results was that students who start tracing and give up do worse on prediction questions than those students who never traced at all. In her ITICSE 2019 paper (see post here), she got the chance to ask those students who stopped tracing why they did. She was extending that with a think-aloud protocol, when something unusual happened. Two data science students, who were successful at programming, frankly refused to trace code.

Her paper “I’m not a computer”: How identity informs value and expectancy during a programming activity is an exploration of why students would flat out refuse to trace code — and yet successfully program. She uses Eccle’s Expectancy Value Theory (which comes up pretty often in our thinking, see this blog post) to describe why the cost of tracing outweighs the utility for these students, which is defined in terms of their sense of identity — what they see themselves doing in the future. Sure, there will be some programs that they won’t be able to debug or understand because they won’t trace line-by-line. But maybe they’ll never actually have to deal with code that complex. Is this so bad?

Katie’s live session is 2:00-2:40pm Eastern time on June 23. The video link will be available on the conference website to registered attendees. A pre-print version of her paper is available here.

Both of these papers give us new insight into the unexpected consequences of how we teach computing. We currently expect students to figure out how their teachers are relating physical space and computation, through metaphors that we don’t typically explain. We currently teach computing expecting students to be able to trace code line-by-line, though some students will not do it (and maybe don’t really need to). If we want to grow who can succeed at computing education, we need to think through who might be struggling with how we’re teaching now, and how we might do better.

June 15, 2020 at 7:00 am 52 comments

An Analysis of Supports and Barriers to Offering Computer Science in Georgia Public High Schools: Miranda Parker’s Defense

Miranda Parker defends her dissertation this Thursday.  It’s a really fascinating story, trying to answer the question: Why does a high school in Georgia decide (or not) to offer computer science?  She did a big regression analysis, and then four detailed case studies.  Readers of this blog will know Miranda from her guest blog post on the Google-Gallup polls, her SCS1 replication of the multi-lingual and validated measure of CS1 knowledge, her study of teacher-student differences in using ebooks, and her work exploring the role of spatial reasoning to relate SES and CS performance (work that was part of her dissertation study). I’m looking forward to flying down to Atlanta and being there to cheer her on to the finish.

Title: An Analysis of Supports and Barriers to Offering Computer Science in Georgia Public High Schools

Miranda Parker
Human-Centered Computing Ph.D. Candidate
School of Interactive Computing
College of Computing
Georgia Institute of Technology

Date: Thursday, October 10, 2019

Time: 10AM to 12PM EST

Location: 85 5th Street NE, Technology Square Research Building (TSRB), 2nd floor, Room 223

Committee:

Dr. Mark Guzdial (Advisor), School of Interactive Computing, Georgia Institute of Technology
Dr. Betsy DiSalvo, School of Interactive Computing, Georgia Institute of Technology
Dr. Rebecca E. Grinter, School of Interactive Computing, Georgia Institute of Technology
Dr. Willie Pearson, Jr., School of History and Sociology, Georgia Institute of Technology
Dr. Leigh Ann DeLyser, CSforAll Consortium

Abstract:

There is a growing international movement to provide every child access to high-quality computing education. Despite the widespread effort, most children in the US do not take any computing classes in primary or secondary schools. There are many factors that principals and districts must consider when determining whether to offer CS courses. The process through which school officials make these decisions, and the supports and barriers they face in the process, is not well understood. Once we understand these supports and barriers, we can better design and implement policy to provide CS for all.

In my thesis, I study public high schools in the state of Georgia and the supports and barriers that affect offerings of CS courses. I quantitatively model school- and county-level factors and the impact these factors have on CS enrollment and offerings. The best regression models include prior CS enrollment or offerings, implying that CS is likely sustainable once a class is offered. However, large unexplained variances persist in the regression models.

To help explain this variance, I selected four high schools and interviewed principals, counselors, and teachers about what helps, or hurts, their decisions to offer a CS course. I build case studies around each school to explore the structural and people-oriented themes the participants discussed. Difficulty in hiring and retaining qualified teachers in CS was one major theme. I frame the case studies using diffusion of innovations providing additional insights into what attributes support a school deciding to offer a CS course.

The qualitative themes gathered from the case studies and the quantitative factors used in the regression models inform a theory of supports and barriers to CS course offerings in high schools in Georgia. This understanding can influence future educational policy decisions around CS education and provide a foundation for future work on schools and CS access.

October 7, 2019 at 7:00 am 1 comment

Social studies teachers programming, when high schools choose to teach CS, and new models of cognition and intelligence in programming: An ICER 2019 Preview

My group will be presenting two posters at ICER this year.

  • Bahare Naimipour (Engineering Education Research PhD student at U-Michigan) will be presenting our participatory design session with social studies educators, Helping Social Studies Teachers to Design Learning Experiences Around Data–Participatory design for new teacher-centric programming languages. We had 18 history and economics teachers building data visualizations in either Vega-Lite or JavaScript with Google Charts. Everyone got the starter visualization running and made changes that they wanted in less than 20 minutes. Those who started in Vega-Lite also tried out the JavaScript code, but only about 1/4 of the JS groups moved to Vega-Lite successfully.
  • Miranda Parker (Human-Centered Computing PhD student at Georgia Tech) will be presenting her quantitative model explaining about half of the variance in whether Georgia high schools taught CS in 2016, A Statewide Quantitative Analysis of Computer Science: What Predicts CS in Georgia Public High School. The most important factor was whether the school taught CS the year before, suggesting that overcoming inertia is a big deal — it’s easier to sustain a CS program than start one. She may talk a little about her new qualitative work, where she’s studying four schools as case studies about their factors in choosing to teach CS, or not.

Barbara is co-author on a paper, A Spaced, Interleaved Retrieval Practice Tool that is Motivating and Effective, with Iman Yeckehzaare and Paul Resnick . This is about a spaced practice tool that 32% of the students in an introductory programming course used more than they needed to, and the number of hours of use had a measurable positive effect on the final exam grade.

All of our other papers were rejected this year, but we’re in good company — the accept rate was around 18%. But I do want to talk about a set of papers that will be presented by others at ICER 2019. These are papers that I heard about, then I asked the authors for copies. I’m excited about all three of them.

How Do Students Talk About Intelligence? An Investigation of Motivation, Self-efficacy, and Mindsets in Computer Science by Jamie Gorson and Eleanor O’Rourke (see released version of the paper here)

One of the persistent questions in computing education research is why growth mindset interventions are not always effective (see blog post here). We get hard-to-interpret results. I met Jamie and Nell at the Northwestern Symposium on Computer Science and the Learning Sciences in April (amazing event, see here for more details). Nell worked with Carol Dweck during her graduate studies.

Jamie and Nell found mixed mindsets among the CS students that they studied. Some of the students they studied had growth mindsets about intelligence, but their talk about programming practices showed more fixed mindset characteristics. Other students self-identified as having some of both growth and fixed mindset beliefs.

In particular, some students talked about intelligence in CS in ways that are unproductive when it came to the practice of programming. For example, some students talked about the best programmers as being able to write the whole code in one sitting, or never getting any errors. A more growth mindset approach to programming would be evidenced by talking about building programs in pieces, expecting errors, and improving through effort over time.

This is a really helpful finding. It gives us new hypotheses to explore about why growth mindset interventions haven’t been as successful in CS as in other disciplines. Few disciplines have this strong distinction between their knowledge and their practice as acutely as we do in CS. It’s no wonder that we see these mixed mindsets.

Toward Context-Dependent Models of Productive Knowledge in Programming Cognition, by Brian A. Danielak

I’ve known Brian since he was a PhD student, and have been hoping that he’d start to publish some of his dissertation work. I got to read one chapter of it, and found it amazingly insightful. Brian explained how what we might see as a “random walk” of syntax was actually purposeful and rational behavior. I was excited to hear about this paper, and I enjoyed reading it.

It’s such an unusual paper for ICER! It’s empirical, but has no methods section. A big part of it is connecting to prior literature, but it’s not about a formal literature review.

Brian is making an argument about how we characterize knowledge and student success in CS. He points out that we often talk about students being wrong and having misconceptions, which is less productive than figuring out what they understand and where their alternative conceptions work or fail. I see his work following on to the work of Rich et al. (mentioned in this blog post) on CS learning trajectories. There are so many things to learn in CS, and sometimes, just getting started on the trajectory is a big step.

Spatial Encoding Strategy Theory: The Relationship between Spatial Skill and STEM Achievement by Lauren Margulieux.

Lauren is doing some impressive theoretical work here. She’s considering the work exploring the relationship between spatial reasoning and CS learning/performance, then constructs a theory explaining the observed results. Since it’s Lauren, the theory is thorough and covers well the known results in this space. I wrote her that I didn’t think that theory explains things that we expect are related to spatial reasoning, but we don’t yet have empirical evidence to support it. For example, when programmers simulate a program in their mind, their mental models may have a spatial component to them, but I don’t know of empirical work that explores that dimension of CS performance. But again, since it’s Lauren, I wouldn’t be surprised if her presentation addresses this point, beyond what was in the paper. (Also, read Lauren’s own summary of the paper here.)

I am looking forward to the discussion of these papers at ICER!

August 12, 2019 at 7:00 am 1 comment

The problem with sorting students into CS classes: We don’t know how, and we may institutionalize inequity

One of the more interesting features of the ACM SIGCSE ITiCSE (Innovation and Technology in CS Education) conference are “working groups” (see description here). Groups of attendees from around the world work together before and at the conference on an issue of interest, then publish a report on what happened. This is the mechanism that Mike McCracken used when he organized the first Multi-Institutional, Multi-National (MIMN) study in CS Ed (see paper here). This year’s Working Group #9 caught my eye (see list here).

The description of what the group wants to explore is interesting: How can we measure what will lead to success in introductory computer science?

The main issues are the following.

  • The ability to predict skill in the absence of prior experience
  • The value of programming language neutrality in an assessment instrument
  • Stigma and other perception issues associated with students’ performance, especially among groups underrepresented in computer science

It’s a deep and interesting question that several research groups have explored. Probably the most famous of these is the “The Camel has Two Humps.” If you read that paper, be sure to read Caspersen et al’s (unsuccessful) attempt to replicate the results (here), Simon’s work with Dehnadi and Bornat to replicate the results (again unsuccessful, here), and then finally the retraction of the original results (here). Bennedsen and Caspersen have a nice survey paper about what we know about predictive factors from ICER 2005, and there was a paper at SIGCSE 2019 that used multiple data sources to predict success in multiple CS courses (here). The questions as I see it are (a) what are the skills and knowledge that improve success in learning to program, (b) how can we measure to determine if they are there, and (c) how can we teach those skills explicitly if they are not.

Elizabeth Patitsas explored the question of whether there are innate differences between students that lead to success or failure in introductory CS (see paper here). She does not prove that there is no so-called Geek Gene. We can’t prove that something does not exist. She does show that (a) that grades at one institution over many years are (mostly) not bimodal, and (b) some faculty see bimodal grade distributions even if the distribution is normal. If there was something else going on (Geek Gene, aptitude, whatever), you wouldn’t expect that much normality. So she gives us evidence to doubt the Geek Gene hypothesis, and she gives us a reasonable alternative hypothesis. But it isn’t definitive proof — that’s what Ahadi and Lister argued at ICER 2013. We have to do more research to better understand the problem.

Are Patitsas’s results suspect because they’re from an elite school? Maybe. Asking that question is really common among CS faculty — Lecia Barker found that that’s one of the top reasons why CS faculty ignore CS Ed research. We discount findings from places unlike ours. That’s why Multi-Institutional, Multi-National (MIMN) is such a brilliant idea. They control for institutional and even national biases. (A MIMN study showing the effectiveness of Peer Instruction is in the top-10 SIGCSE papers list.)

In my research group, we’re exploring spatial reasoning as one of those skills that may be foundational (though we don’t yet know how or why). We can measure spatial reasoning, and that we can (pretty easily) teach. We have empirically shown that wealth (more specifically, socioeconomic status (SES)) leads to success in computing (see this paper), and we have a literature review identifying other privileges that likely lead to success in CS (see Miranda Parker’s lit review).

I am concerned about the goals of the working group. The title is “Towards an Ability to Direct College Students to an Appropriately Paced Introductory Computer Science Course.” The first line of the description is:

We propose a working group to investigate methods of proper placement of university entrance-level students into introductory computer science courses.

The idea is that we might have two (or more) different intro courses, one at a normal pace and one at a remedial pace. The overall goal is to meet student needs. There is good evidence that having different intro courses is a good practice. Most institutions that I know that have multiple introductory courses choose based on major, or allow students to choose based on interest or on expectations of abilities. It’s a different thing to have different courses assigned by test.

If we don’t know what those skills are that might predict success in CS, how are you going to measure them? And if you do build a test that sorts students, what will you actually be sorting on?  It’s hard to build a CS placement test that doesn’t actually sort on wealth, prior experience, and other forms of privilege.

If the test sorts on privilege, it is institutionalizing inequity. Poor kids go into one pile, and rich kids go into the other. Kids who have access to CS education go into one pile, everyone else into another.

Why build the test at all?  To build a test to sort people into classes presumes that there are differences that cannot be mitigated by teaching. Building such a test presumes that there is a constant answer to “What does it take to succeed in CS1?” If we had such a test, would the results be predictive for classes that both use and don’t use pair programming? Peer instruction? Parsons problems?

I suggest a different perspective on the problem.  We can get so much further if we instead improve success in CS1. Let’s make the introductory course one that more students will succeed in.

There’s so much evidence that we can improve success rates with better teaching methods and revised curriculum. Beth Simon, Leo Porter, Cynthia Lee, and I have been teaching workshops the last four years to new CS faculty on how to teach better. It works — I learned Peer Instruction from them, and use it successfully today. My read on the existing literature suggests that everyone benefits from active learning, and the less privileged students benefit the most (see Annie-Murphy Paul’s articles).

One of the reasons why spatial reasoning is so interesting to explore is that (a) it does seem related to learning computing and (b) it is teachable.  Several researchers have shown that spatial skills can be increased relatively easily, and that the improved skills are long-lasting and do transfer to new contexts. Rather than sort people, we’re better off teaching people the skills that we want them to have, using research-informed methods that have measurable effects.

Bottom line: We are dealing with extraordinary enrollment pressures in CS right now. We do need to try different things, and multiple introductory courses is a good idea. Let’s manage the enrollment with the best of our research results. Let’s avoid institutionalizing inequities.

April 1, 2019 at 7:00 am 10 comments

Adaptive Parsons problems, and the role of SES and Gesture in learning computing: ICER 2018 Preview

 

Next week is the 2018 International Computing Education Research Conference in Espoo, Finland. The proceedings are (as of this writing) available here: https://dl.acm.org/citation.cfm?id=3230977. Our group has three papers in the 28 accepted this year.

“Evaluating the efficiency and effectiveness of adaptive Parsons problems” by Barbara Ericson, Jim Foley, and Jochen (“Jeff”) Rick

These are the final studies from Barb Ericson’s dissertation (I blogged about her defense here). In her experiment, she compared four conditions: Students learning through writing code, through fixing code, through solving Parsons problems, and through solving her new adaptive Parsons problems. She had a control group this time (different from her Koli Calling paper) that did turtle graphics between the pre-test and post-test, so that she could be sure that there wasn’t just a testing effect of pre-test followed by a post-test. The bottom line was basically what she predicted: Learning did occur, with no significant difference between treatment groups, but the Parsons problems groups took less time. Our ebooks now include some of her adaptive Parsons problems, so she can compare performance across many students on adaptive and non-adaptive forms of the same problem. She finds that students solve the problems more and with fewer trials on the adaptive problems. So, adaptive Parsons problems lead to the same amount of learning, in less time, with fewer failures. (Failures matter, since self-efficacy is a big deal in computer science education.)

“Socioeconomic status and Computer science achievement: Spatial ability as a mediating variable in a novel model of understanding” by Miranda Parker, Amber Solomon, Brianna Pritchett, David Illingworth, Lauren Margulieux, and Mark Guzdial

(Link to last version I reviewed.)

This study is a response to the paper Steve Cooper presented at ICER 2015 (see blog post here), where they found that spatial reasoning training erased performance differences between higher and lower socioeconomic status (SES) students, while the comparison class had higher-SES students performing better than lower-SES students. Miranda and Amber wanted to test this relationship at a larger scale.

Why should wealthier students do better in CS? The most common reason I’ve heard is that wealthier students have more opportunities to study CS — they have greater access. Sometimes that’s called preparatory privilege.

Miranda and Amber and their team wanted to test whether access is really the right intermediate variable. They gave students at two different Universities four tests:

  • Part of Miranda’s SCS1 to measure performance in CS.
  • A standardized test of SES.
  • A test of spatial reasoning.
  • A survey about the amount of access they had to CS education, e.g., formal classes, code clubs, summer camps, etc.

David and Lauren did the factor analysis and structural equation modeling to compare two hypotheses: Does higher SES lead to greater access which leads to greater success in CS, or does higher SES lead to higher spatial reasoning which leads to greater success in CS? Neither hypothesis accounted for a significant amount of the differences in CS performance, but the spatial reasoning model did better than the access model.

There are some significant limitations of this study. The biggest is that they gathered data at universities. A lot of SES variance just disappears when you look at college students — they tend to be wealthier than average.

Still, the result is important for challenging the prevailing assumption about why wealthier kids do better in CS. More, spatial reasoning is an interesting variable because it’s rather inexpensively taught. It’s expensive to prepare CS teachers and get them into all schools. Steve showed that we can teach spatial reasoning within an existing CS class and reduce SES differences.

“Applying a Gesture Taxonomy to Introductory Computing Concepts” by Amber Solomon, Betsy DiSalvo, Mark Guzdial, and Ben Shapiro

(Link to last version I saw.)

We were a bit surprised (quite pleasantly!) that this paper got into ICER. I love the paper, but it’s different from most ICER papers.

Amber is interested in the role that gestures play in teaching CS. She started this paper from a taxonomy of gestures seen in other STEM classes. She observed a CS classroom and used her observations to provide concrete examples of the gestures seen in other kinds of classes. This isn’t a report of empirical findings. This is a report of using a lens borrowed from another field to look at CS learning and teaching in a new way.

My favorite part of of this paper is when Amber points out what parts of CS gestures don’t really fit in the taxonomy. It’s one thing to point to lines of code – that’s relatively concrete. It’s another thing to “point” to reference data, e.g., when explaining a sort and you gesture at the two elements you’re comparing or swapping. What exactly/concretely are we pointing at? Arrays are neither horizontal nor vertical — that distinction doesn’t really exist in memory. Arrays have no physical representation, but we act (usually) as if they’re laid out horizontally in front of us. What assumptions are we making in order to use gestures in our teaching? And what if students don’t share in those assumptions?

August 10, 2018 at 7:00 am 7 comments

Teachers are not the same as students, and the role of tracing: ICER 2017 Preview

The International Computing Education Research conference starts today at the University of Washington in Tacoma. You can find the conference schedule here, and all the proceedings in the ACM Digital Library here. In past years, all the papers have been free for the first couple weeks after the conference, so grab them while they are outside the paywall.

Yesterday was the Doctoral Consortium, which had a significant Georgia Tech presence. My colleague Betsy DiSalvo was one of the discussants. Two of my PhD students were participants:

We have two research papers being presented at ICER this year. Miranda Parker and Kantwon Rogers will be presenting Students and Teachers Use An Online AP CS Principles EBook Differently: Teacher Behavior Consistent with Expert Learners (see paper here) which is from Miranda C. Parker, Kantwon Rogers, Barbara J. Ericson, and me. Miranda and Kantwon studied the ebooks that we've been creating for AP CSP teachers and students (see links here). They're asking a big question: "Can we develop one set of material for both high school teachers and students, or do they need different kinds of materials?" First, they showed that there was statistically significantly different behaviors between teachers and students (e.g. different number of interactions with different types of activities). Then, they tried to explain why there were differences.

We develop a model of teachers as expert learners (e.g., they know more knowledge so they can create more linkages, they know how to learn, they know better how to monitor their learning) and high school students as more novice learners. They dig into the log file data to find evidence consistent with that explanation. For example, students repeatedly try to solve Parsons problems long after they are likely to get it right and learn from it, while teachers move along when they get stuck. Students are more likely to run code and then run it again (with no edits in between) than teachers. At the end of the paper, they offer design suggestions based on this model for how we might develop learning materials designed explicitly for teachers vs. students.

Katie Cunningham will be presenting Using Tracing and Sketching to Solve Programming Problems: Replicating and Extending an Analysis of What Students Draw (see paper here) which is from Kathryn Cunningham, Sarah Blanchard, Barbara Ericson, and me. The big question here is: "Of what use is paper-and-pen based sketching/tracing for CS students?" Several years ago, the Leeds' Working Group (at ITiCSE 2004) did a multi-national study of how students solved complicated problems with iteration, and they collected the students' scrap paper. (You can find a copy of the paper here.) They found (not surprisingly) that students who traced code were far more likely to get the problems right. Barb was doing an experiment for her study of Parsons Problems, and gave scrap paper to students, which Katie and Sarah analyzed.

First, they replicate the Leeds' Working Group study. Those who trace do better on problems where they have to predict the behavior of the code. Already, it's a good result. But then, Katie and Sarah go further. For example, they find it's not always true. If a problem is pretty easy, those who trace are actually more likely to get it wrong, so the correlation goes the other way. And those who start to trace but then give up are even more likely to get it wrong than those who never traced at all.

They also start to ask a tantalizing question: Where did these tracing methods come from? A method is only useful if it gets used — what leads to use? Katie interviewed the two teachers of the class (each taught about half of the 100+ students in the study). Both teachers did tracing in class. Teacher A's method gets used by some students. Teacher B's method gets used by no students! Instead, some students use the method taught by the head Teaching Assistant. Why do some students pick up a tracing method, and why do they adopt the one that they do? Because it's easier to remember? Because it's more likely to lead to a right answer? Because they trust the person who taught it? More to explore on that one.

August 18, 2017 at 7:00 am 7 comments

Katie Cunningham receives NSF fellowship: Studying how CS students use sketching and tracing

Kate Cunningham is a first year PhD student working with me in computing education research.  She just won an NSF graduate research fellowship, and the College of Computing interviewed her. She explains the direction that she’s exploring now, which I think is super exciting.

“I’m interested in examining the kinds of things students draw and sketch when they trace through code,” she said. “Can certain types of sketching help students do better when they learn introductory programming?”  She grew interested in this topic while working as a teacher for a program in California. As she watched students there work with code, she found that they worked solely with the numbers and text on their computer screen.“They weren’t really drawing,” she said. “I found that the drawing techniques we encouraged were really useful for those students, so I was inspired to study it at Georgia Tech.”

Essentially, the idea is that by drawing or sketching a visual representation of their work as they code, students may be able to better understand the operations of how the computer works. “It’s a term we call the ‘notional machine,’” Cunningham explained. “It’s this idea of how the computer processes the instructions. I think if students are drawing out the process for how their code is working, that can help them to fully understand how the instructions are working.” That’s one benefit. Another, she said, is better collaboration. If a student is sketching the process, she posits, the teacher can better see and understand what they’re thinking.

Source: IC Ph.D. student Katie Cunningham receives NSF fellowship | College of Computing

April 17, 2017 at 7:00 am 2 comments

Steps to Help Foster a Preschooler’s Spatial Reasoning Skills: And Computer Science students, too?

I am a fan of the work at the Spatial Intelligence and Learning Center (see web page here). They’re in the final phases of the Center and are starting to publish wrap-up papers. Since spatial intelligence is likely predictive of success in computing (see paper from last year’s ICER), these are important ideas for us to think about in computing education, too.

Reading spatially challenging picture books is another way to engage children’s spatial thinking and expose them to spatial language.  Look for books that include pictures from various angles or perspectives, that contain maps and abundant spatial language, or whose illustration require close attention to decipher their meaning — such as wordless books. According to Newcombe, “Even though books only contain static pictures, they can help children understand spatial transformations, if adults read them with the children and stimulate their imagination.”

Source: Steps to Help Foster a Preschooler’s Spatial Reasoning Skills | MindShift | KQED News

April 6, 2016 at 8:03 am Leave a comment

Spatial Visualization Skills FAQs – Engage Engineering

A cool FAQ on the importance of spatial visualization skills in most STEM fields, and the research on how to improve them.

Research has demonstrated that training is an effective way to improve spatial visualization skills Contero et al., 2006; Ferguson, Ball, McDaniel, & Anderson, 2008; Hand, Uttal, Marulis, & Newcombe, 2008; Hsi et al., 1997; Martín-Dorta et al., 2008; Newcombe, 2006; Onyancha, Derov, & Kinsey, 2009; Onyancha, R., Towle, E., & Kinsey, B., 2007; Sorby, 2009; Sorby & Baartmans, 2000; Terlecki, Newcombe, & Little, 2008.  In the area of mental rotation where the largest gender gap in performance exists, training has been effective as well Sorby & Baartmans, 2000; Sorby, Drummer, Hungwe, Parolini, & Molzan, 2006.In one study, students who failed the Purdue Spatial Visualization Test PSVT:R and enrolled in spatial skills training were able to improve their scores on the mental rotation test from approximately 50% to 77% or higher than students who failed the test and did not enroll in the course. These students also got better grades in 1st year STEM courses Sorby, 2009.

via Spatial Visualization Skills FAQs – Engage Engineering.

January 21, 2015 at 8:34 am Leave a comment

Visual ability predicts a computer science career: Why? And can we use that to improve learning?

I’ve raised this question before, but since I just saw Nora Newcombe speak at NCWIT, I thought it was worth raising the issue again. Here’s my picture of one of her slides — could definitely have used jitter-removal on my camera, but I hope it’s clear enough to make the point.

This is from a longitudinal study, testing students’ visual ability, then tracking what fields they go into later. Having significant visual ability most strongly predicts an Engineering career, but in second place (and really close) is “Mathematics and Computer Science.” That score at the bottom is worth noting: Having significant visual ability is negatively correlated with going into Education. Nora points out that this is a significant problem. Visual skills are not fixed. Training in visual skills improves those skills, and the effect is durable and transferable. But, the researchers at SILC found that teachers with low visual skills had more anxiety about teaching visual skills, and those teachers depressed the impact on their students. A key part of Nora’s talk was showing how the gender gap in visual skills can be easily reduced with training (relating to the earlier discussion about intelligence), such that women perform just as well as men.

The Spatial Intelligence and Learning Center (SILC) is now its sixth year of a ten year program. I don’t think that they’re going to get to computer science before the 10th year, but I hope that someone does. The results in mathematics alone are fascinating and suggest some significant interventions for computer science. For example, Nora mentioned an in-press paper by Sheryl Sorby showing how teaching students how to improve their spatial skills improved their performance in Calculus, and I have heard that she has similar results about computer science. Could we improve learning in computer science (especially data structures) by teaching spatial skills first?

May 29, 2012 at 6:57 am 14 comments

Science of Spatial Learning: Nora Newcombe at NCWIT

Great to see this coverage of SILC in US News and World Report, and I’m excited to hear Dr. Nora Newcombe speak at the NCWIT Summit Tuesday of this week. As I’ve mentioned previously, SILC hasn’t looked much at computer science yet, but there are lots of reasons to think that spatial learning plays an important role in computing education.

Spatial reasoning, which is the ability to mentally visualize and manipulate two- and three-dimensional objects, also is a great predictor of talent in science, technology, engineering and math, collectively known as STEM.

Yet, “these skills are not valued in our society or taught adequately in the educational system,” says Newcombe, who also is principal investigator for the Spatial Intelligence and Learning Center.  “People will readily say such things as ‘I hate math,’ or ‘I can’t find my way when I’m lost,’ and think it’s cute, whereas they would be embarrassed to say ‘I can’t read.’

“People have a theory about this skill, that it’s innate at birth and you can’t develop it, and that’s really not true,” she adds. “It’s probably true that some people are born with a better ability to take in spatial information, but that doesn’t mean if you aren’t born with it, you can’t change.  The brain has a certain amount of plasticity.”

via Science of Spatial Learning – US News and World Report.

May 21, 2012 at 12:09 pm 3 comments


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