*this paper won the Chair’s Best Paper Award at the 2018 International Computer Education Research conference
To propose a theoretical model of the relationship between spatial skills and computer science (mostly programming) performance and explore the cognitive processes that contribute to both.
Spatial Skill and STEM Performance
Spatial skill is a person’s accuracy and time to complete spatial reasoning tasks, which represent subskills and include
- spatial visualization – mental rotation or transformation of objects
- spatial relations – understanding relationships to landmarks or orientations of objects, such as using a map
- closure speed – completing a partially obscured or incomplete pattern
- closure flexibility – identifying a pattern that is partially obscured or incomplete
- perceptual speed – identifying an unobscured pattern
- visual imagery – translating text or verbal representations to graphic or symbolic representations
People who have higher spatial skill perform better in all kinds of STEM domains, including chemisty, physics, engineering, and programming (Wai et al., 2009). By itself, this correlation might not be that important for STEM education. However, training people to improve their spatial skill has a consistent positive effect on their STEM performance (Sorby, 2009; Uttal et al., 2013), offering students and instructors a unique way to improve or to overcome initial deficits. Recently, this connection between spatial skill and STEM performance has gained more and more attention from the computing education community, especially for helping students to prepare and succeed in intro programming courses.
Proposed Theoretical Model
Parkinson and Cutts’ theoretical model (Figure 8) maps the connections between spatial subskills and components of programming education. So far the connections are based mostly on their best logic and guesses, but by putting forward a model, researchers can now test these connections to determine whether they are valid or not. All of the connections can get complicated, but the broad strokes are that spatial visualization is related to focal generation of programs, development of a notional machine, and to matching schema to problems. The next set of spatial subskills–closure speed, closure flexibility, and perceptual speed–are said to be related to matching schema to problems and to low-level conceptions of code (i.e., individual words or statements, atoms in the Block Model). The last spatial subskill in the model, spatial relations, is connected to the relations between low- and mid-level conceptions of code (i.e., atoms and blocks from the Block Model).
Why this is important
Though Parkinson and Cutts’ paper offer several valuable pieces, I find their catalog of different spatial subskills, the measurements used to assess the subskills, and their proposed model of the relationship between spatial subskills and aspects of programming education to be the most valuable. By breaking apart spatial skill, as cognitive scientists have for decades, Parkinson and Cutts have provided a model that discriminates among subskills and, thus, encourages future research to be more targetted and nuanced. The other side of the model breaks apart programming education into subskills and the existing theories and models for learning to program. Therefore, their theoretical model has created a high fidelity map of the connections between spatial subskills and programming education that allows researchers to draw from what we already know about these two areas to figure out how they interact with each other. Whether I or anyone else agrees with each of the connections that they have made doesn’t matter because now we have a detailed theoretical model that can be empirically tested, and we can let the data tell us what is right or wrong.
Parkinson, J., & Cutts, Q. (2018). Investigating the Relationship Between Spatial Skills and Computer Science. In Proceedings of the 2018 ACM Conference on International Computing Education Research (pp. 106-114). ACM.
Sorby, S. A. (2009). Educational research in developing 3‐D spatial skills for engineering students. International Journal of Science Education, 31(3), 459-480.
Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., & Newcombe, N. S. (2013). The malleability of spatial skills: A meta-analysis of training studies. Psychological Bulletin, 139(2), 352.
Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology, 101(4), 817.
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