research-article

Can Students' Spatial Skills Predict Their Programming Abilities?

Authors:

Stephen Cooper,

Jonathan Gratch,

Mohsen DorodchiAuthors Info & Claims

ITiCSE '20: Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education

Pages 446 - 451

https://doi.org/10.1145/3341525.3387380

Published: 15 June 2020 Publication History

Abstract

Spatial abilities have been shown to have high predictability in students' success in STEM related fields. Studies have also shown that there is a correlation between students' spatial skills and programming abilities, but it is unknown how well students' prior spatial abilities can predict students' introductory programming abilities at the end of the semester. During this study we used a multinomal logistic regression to create a predictive model to predict students' introductory programming abilities at the end of the semester. The highest model accuracy (64.6%) was obtained when accounting for students' prior programming abilities, prior spatial skills, socioeconomic status, and three factors regarding students' attitudes towards computing. It was also found that when looking at the predictability of each individual variable, students' prior spatial ability had the highest predictability (56.6% accuracy) when compared to all other variables.

References

[1]

H-D Barker and T. Engida. 2001. Structural chemistry and spatial ability indifferent cultures. Chemistry Education: Research and Practice in Europe 2, 3(2001), 227--239.

[2]

R. Bockmon, S. Cooper, W. Koperski, J. Gratch, S. Sorby, and M. Dorodchi. 2020. A CS1 spatial skills intervention and the impact on introductory programming abilties. Proceedings of the 51th ACM Technical Symposium on Computer Science Education (SIGCSE '20)(2020).

Digital Library

[3]

R. Bockmon, S. Cooper, J. Gratch, and M. Dorodchi. 2019. (Re)Validating Cognitive Introductory Computing Instruments. Proceedings of the 50th ACM Technical Symposium on Computer Science Education(2019), 552--557.

[4]

P. Byrne and G. Lyons. 2001. The Effect of Student Attributes on Success in Programming. ACM SIGCSE Bulletin 33, 3 (2001), 49--52.

Digital Library

[5]

C. S. Carter, M. A. LaRussa, and G. M. Bodner. 1987. A study of two measures of spatial ability as predictors of success in different levels of general chemistry. Journal of Research in Science Teaching 24 (1987), 645--657.

[6]

S. Cooper, K. Wang, I. Maya, and S. Sorby. 2015. Spatial skill training in introductory computing. ICER '15(2015), 13--20.

[7]

A. Cox, M. Fisher, and P. O'Brien. 2005. Theoretical considerations on navigating codespace with spatial cognition. 17th Workshop of the Psychology of Programming Interest Group(2005).

[8]

R. D'Agostino and E. S. Pearson. 1973. Tests for departure from normality. Biometrika, 60 (1973), 613--622.

[9]

B. Dorn and A. Tew. 2015. Empirical validation and application of the computing attitudes survey. Computer Science Education 25, 1 (2015), 1--36.

[10]

S. Fincher, B. Baker, I. Box, Q. Cutts, M. de Raadt, P. Haden, J. Hamer, M. Hamilton,R. Lister, and M. Petre. [n. d.]. Programmed to succeed? A multi-national, multi-institutional study of introductory programming courses. University of Kent, Computing Laboratory Technical Report 1-05([n. d.]).

[11]

M. Fisher, A. Cox, and L. Zhao. 2006. Using sex differences to link spatial cognition and program comprehension. In Proceedings of the 22nd IEEE International Conference on Software Maintenance (ICSM)(2006), 289--298.

[12]

S. Jones and G. Burnett. 2007. Spatial skills and navigation of source code. Proceedings of ITiCSE 2007(2007), 231--235.

[13]

S. Jones and G. Burnett. 2008. Spatial skills and learning to program. Human Technology 4, 1 (2008), 47--61.

[14]

L. Lacher, A. Jiang, Y. Zhang, and M. Lewis. 2017. Aptitude and previous experience in cs1 classes. Int'l Conf. Frontiers in Education: CS and CE17 (2017),87--91.

[15]

S. N. Liao, D. Singaro, C. Alvarado, Griswold W. G, and L. Porter. 2019. Exploring the value of different data sources for predicting student performance in multiple CS courses. SIGCSE '19(2019), 112--118.

[16]

R. E. Mayer, J. L. Dyck, and W. Vilberg. 1986. Learning to program and learning to think: What's the connection? Commun. ACM29 (1986), 605--610.

[17]

K. Norman. 1994. Spatial visualization-a gateway to computer-based technology. Journal of Special Education Technology 12, 3 (1994), 195--206.

[18]

M. Parker, M. Guzdial, and S. Engleman. 2016. Replication, validation, and use of a language independent cs1 knowledge assessment. ICER '16(2016), 93--101.

[19]

M. Parker, A. Solomon, B. Pritchett, D. Illingworth, L. Margulieux, and M. Guzdial. 2018. Socioeconomic status and computer science achievement. ICER '18(2018),97--105.

[20]

J. Parkinson and Q. Cutts. 2018. Investigating the relationship between spatial skills and compute science. ICER '18(2018), 106--114.

[21]

N. Rountree, J. Rountree, and A. Robins. 2002. Predictors of Success and Failure in a CS1 Course. ACM SIGCSE Bulletin 34, 4 (2002), 121--124.

Digital Library

[22]

D. L. Shea, D. Lubinski, and P. B. Camilla. 2001. Importance of assessing spatialability in intellectually talented young adolescents: a 20-year longitudinal study.Journal of Educational Psychology93, 3 (2001), 604--614.

[23]

I. M. Smith. 1964. Spatial ability-its educational and social significance. London : University of London.

[24]

S. A. Sorby. 2000. Spatial abilities and their relationship to effective learning of 3-d modeling software. Engineering Desing Graphics Journal 64, 3 (2000), 30--35.

[25]

K. J. Vicente, B. C. Hayes, and R. C. Williges. 1987. Assaying and isolating individual differences in searching a hierarchical file system.Human Factors29(1987), 117--124.

[26]

R. L. Wasserstein and N. A. Lazar. 2016. The ASA's Statement on p-Values: Context, Process, and Purpose. The American Statistician 70, 2 (2016), 129--133.

[27]

C. Watson and F. W. Li. [n. d.]. Failure rates in introductory programming revisited. ([n. d.]).

[28]

N. Webb. 1984. Microcomputer learning in small groups: Cognitive requirements and group processes.Journal of Educational Psychology 76 (1984), 1076--1088.

[29]

L. H. Werth. 1986. Predicting student performance in a beginning computer science class. Proceedings of the seventeenth SIGCSE technical symposium on Computer science education(1986), 138--143.

Digital Library

[30]

S. Wiedenbeck, B. LaBelle, and V. Kain. 2004. Factors affecting course outcomes in introductory programming. Workshop of the Psychology of Programming Interest Group(2004).

[31]

B. C. Wilson and S. Shrock. 2001. Contributing to success in an introductory computer science course: a study of twelve factors. SIGCSE 2001(2001), 184--188.

[32]

S. Y. Yoon. 2011. Revised Purdue Spatial Visualization Test: Visualization of rotations (Revised PSVT:R) [Psychometric Instrument]. (2011).

Cited By

Bubnic BMernik MKosar T(2024)Exploring the Predictive Potential of Complex Problem-Solving in Computing Education: A Case Study in the Introductory Programming CourseMathematics10.3390/math1211165512:11(1655)Online publication date: 24-May-2024
https://doi.org/10.3390/math12111655
C. Parker MMarsh JNguyen ASalazar CVehouni G(2024)The Effect (or Lack Thereof) of Spatial Skills Training in a Mid-Major Computing CourseProceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699566(1-11)Online publication date: 12-Nov-2024
https://dl.acm.org/doi/10.1145/3699538.3699566
Parkinson JMargulieux L(2024)Improving CS Performance by Developing Spatial SkillsCommunications of the ACM10.1145/368040968:1(68-75)Online publication date: 11-Dec-2024
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Index Terms

Can Students' Spatial Skills Predict Their Programming Abilities?
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
        CS1

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cover image ACM Conferences

ITiCSE '20: Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education

June 2020

615 pages

ISBN:9781450368742

DOI:10.1145/3341525

General Chairs:
Michail Giannakos
NTNU, Norway
,
Guttorm Sindre
Excited/NTNU, Norway
,
Program Chairs:
Andrew Luxton-Reilly
University of Auckland, NZ
,
Monica Divitini
NTNU, Norway

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Publication History

Published: 15 June 2020

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National Science Foundation

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ITiCSE '20

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ITiCSE '20: Innovation and Technology in Computer Science Education

June 15 - 19, 2020

Trondheim, Norway

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Overall Acceptance Rate 552 of 1,613 submissions, 34%

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Cited By

Bubnic BMernik MKosar T(2024)Exploring the Predictive Potential of Complex Problem-Solving in Computing Education: A Case Study in the Introductory Programming CourseMathematics10.3390/math1211165512:11(1655)Online publication date: 24-May-2024
https://doi.org/10.3390/math12111655
C. Parker MMarsh JNguyen ASalazar CVehouni G(2024)The Effect (or Lack Thereof) of Spatial Skills Training in a Mid-Major Computing CourseProceedings of the 24th Koli Calling International Conference on Computing Education Research10.1145/3699538.3699566(1-11)Online publication date: 12-Nov-2024
https://dl.acm.org/doi/10.1145/3699538.3699566
Parkinson JMargulieux L(2024)Improving CS Performance by Developing Spatial SkillsCommunications of the ACM10.1145/368040968:1(68-75)Online publication date: 11-Dec-2024
https://dl.acm.org/doi/10.1145/3680409
Campbell OSanusi IAtagana HMonga MLonati VBarendsen ESheard JPaterson J(2024)From Visual Arts to Programming: Exploring the Impact on Achievement in Constructionist College CS1 ClassesProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653597(604-610)Online publication date: 3-Jul-2024
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Parkinson J(2024)Associations between Secondary School Students' Spatial Skills and Teacher Perceptions of CS Engagement and AptitudeProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 210.1145/3632621.3671426(537-538)Online publication date: 12-Aug-2024
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Salguero AVillegas Molina IElizabeth Margulieux LCutts QPorter LStephenson BStone JBattestilli LRebelsky SShoop L(2024)Applying CS0/CS1 Student Success Factors and Outcomes to Biggs' 3P Educational ModelProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630781(1168-1174)Online publication date: 7-Mar-2024
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Lin TBuckley JGumaelius LAmpadu E(2024)Locating the Potential Development of Spatial ability in the Swedish National CurriculumHeliyon10.1016/j.heliyon.2024.e38356(e38356)Online publication date: Sep-2024
https://doi.org/10.1016/j.heliyon.2024.e38356
Parkinson JCutts Q(2023)Understanding Spatial Skills and Encoding Strategies in Student Problem Solving ActivitiesProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600134(134-147)Online publication date: 7-Aug-2023
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Parkinson JDziallas SMcNeill FWilliams J(2023)Exploring Models and Theories of Spatial Skills in CS through a Multi-National StudyProceedings of the 2023 ACM Conference on International Computing Education Research - Volume 110.1145/3568813.3600129(122-133)Online publication date: 7-Aug-2023
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Salguero A(2023)Establishing an Empirical Foundation for a Theory of Student Learning and Success in CS1Proceedings of the 2023 ACM Conference on International Computing Education Research - Volume 210.1145/3568812.3603444(52-54)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3568812.3603444

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