Visual ability predicts a computer science career: Why? And can we use that to improve learning?
May 29, 2012 at 6:57 am 14 comments
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?
Entry filed under: Uncategorized. Tags: computing education, computing education research, CS1, spatial learning, spatial reasoning, visualization.
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Computing Ed Research - Guzdial's Take 
1.
Darrin Thompson | May 29, 2012 at 8:10 am
What does training in “visual skills” look like?
2.
Mark Guzdial | May 29, 2012 at 8:40 am
http://spatiallearning.org/index.php/initiatives/initiative-4-educate for more on how to educate, and http://spatiallearning.org/index.php/resources/testsainstruments for more on how to measure what has been learned.
3.
Darrin Thompson | May 29, 2012 at 8:52 am
Thanks.
4. Visual ability predicts a computer science career: Why? And can we use that to improve learning? « Computing Education Blog | The Becker Blog | May 29, 2012 at 10:29 am
[…] Visual ability predicts a computer science career: Why? And can we use that to improve learning? « …. Be the first to like. Like Unlike This entry was posted in General and tagged Education, […]
5.
slger | May 29, 2012 at 11:16 am
Some definition questions arise? Does “visual thinking” require sensory perception, i.e. eyesight? Is “visual thinking” the same as “spatial reasoning”? Are congenitally blind students included in these studies? Have the skills of visually impaired computer scientists been analyzed regarding spatial reasoning?
Titles like this research could tinge the perceptions of career possibilities for students who simply have different sensory or motor or cognitive skills than the euro-typical computer scientist. As a late life vision loser, I have met and revere developers of software such as the award-winning NVDA screen reader, the APH/Levelstar mo vile manager, the Yahoo web site, as well as the ecosystem of accessible IOS apps (see AppleVis.com).
The common core of these wonderful appliances is the ability to grasp and manipulate the semantic essence of the application, cope with myriad details, and express the user needs in combinations of tactile and aural means with vision limitations only at maps and diagrams. This is “computational thinking”!!
Speaking of diagrams, http://DiagramCenter.org offers a volunteer opportunity for sighted STEM professionals to annotate diagrams present in digital versions of K12 textbooks. The parent organization Benetech operates http://Bookshare.org which distributes textbooks to U.S. educational institutions, as well as a growing library of volunteer and publisher submitted reading material. Indeed, these texts with/without diagram explanations could participate in career projecting research. Notably Oreilly donates its electronic texts too Bookshare, so just about any programming subject is represented.
Thanks, Mark, if you can promote some attention to the Diagram project.
Susan
http://asyourworldchanges.wordpress.com
6.
chaikens | June 1, 2012 at 11:06 pm
Susan,
I took a look at http://DiagramCenter.org
Their product guide unfortunately omits
http://emacspeak.sourceforge.net/
a free/open source audio desktop system (including ebook readers) originated by blind computer scientist that is tuned to software developers, etc.
Hope this pointer helps your project and others of this blog’s readers.
Seth
7.
John A. Tamplin | May 29, 2012 at 11:22 am
Note that visual ability != spatial ability — those are two different things.
8.
Mark Guzdial | May 29, 2012 at 11:25 am
Agreed, John. I should have been more careful in my blog post title, as Susan is pointing as well. Nora is explicitly addressing spatial ability in this slide, which is typically measured with visual tests (e.g., here’s one 3-D figure, and here are four rotated forms — which of the rotated forms could possibly be the same as the original 3-D figure).
9.
gasstationwithoutpumps | May 29, 2012 at 12:02 pm
There are several different spatial skills that tend to get lumped together in tests. I’m aware of some of the differences, because I have high skill levels in some and low in others. For example, I was always very good at the “which of these rotated objects is the same” tests, but very poor at anything that required visual memory for more than a few seconds. I’m good at reading maps, but very poor at recognizing faces. I have a real hard time judging the sizes of objects except in direct side-by-side comparison. My memory of visual things (what little I have) tends to be more topological than geometric.
So I’d want to know which spatial skills are associated with high levels of engineering, CS, and math. I suspect that they are the ones that rely on abstraction, rather than the ones that rely on memorization, and that it is not the “spatial” nature of the skill that matters so much as the practice at abstraction.
Incidentally, I believe that young kids can be taught to recognize rotated images much better than average by practice—particularly practice at reading upside down and in unusual orientations.
10.
Mark Guzdial | May 29, 2012 at 12:23 pm
Nora said that there are four typical kinds of spatial skills measured: Mental rotation, mechanical reasoning (e.g., given a picture with gears, what’s going to happen when this gear turns?), paper folding, and abstract reasoning. Paper folding and abstract reasoning have no sex differences. There is a male-female gap only on the first two, and differences in mechanical reasoning might be due to cultural issues (e.g., men are expected to play with gears more). She didn’t say which were measured in the longitudinal study.
11.
Bri Morrison | May 29, 2012 at 2:03 pm
If I had been given these kinds of tests and told I needed to do well to go into computer science, I wouldn’t be here. I have *awful* spatial reasoning (my geometry, trigonometry, and physics grades will attest), but still consider myself an above average computer scientist based on logic and coding skills.
I wonder if you tested existing computing professionals of both sexes what you find. I doubt they all have great spatial skills. All this study tells us is that females don’t necessarily perform well on these spatial tests and also don’t go into STEM fields (including CS). But it in no way says that improving spatial skills in women will result in more women in STEM.
12.
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