Posts tagged ‘spatial reasoning’

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 1 comment

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 1 comment

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 2 comments


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