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Investigating the Relationship Between Spatial Skills and Computer Science

Jack Parkinson, Quintin I. Cutts
Publikationsdatum:
Zu finden in: ICER 2018 (Seite 106 bis 114), 2018
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In this paper we have reviewed literature concerning the relationship between SS and STEM, particularly in CS. This literature indicates that a correlation between SS and CS exists, with one study displaying what has been interpreted as a causal effect. Furthermore, we have identified that in one STEM field, SS improve over a period of learning - not as much as if they had received directed SS training, but more than a liberal arts student - which indicates that the relationship is likely to be a biased two-way relationship. We have also collated and presented a substantial discussion of spatial skills themselves, condensing and summarising a broad field in a format which is easy to grasp for the relatively uninitiated. Based on this, we have presented a model for the relationship between SS and CS. This model is rooted in existing research into cognition in CS, particularly in program comprehension, program generation and problem comprehension. The model indicates that particular factors of SS are likely to have an effect in the reading and identification of key points in code or problems, as well as the mental models constructed in attempting to understand programs and theoretical problems.
Von Jack Parkinson, Quintin I. Cutts im Konferenz-Band ICER 2018 im Text Investigating the Relationship Between Spatial Skills and Computer Science (2018)

The relationship between spatial skills training and computer science learning is unclear. Reported experiments provide tantalising, though not convincing, evidence that training a programming student's spatial skills may accelerate the development of their programming skills. Given the well-documented challenge of learning to program, such acceleration would be welcomed. Despite the experimental results, no attempt has been made to develop a model of how a linkage between spatial skills and computer science ability might operate, hampering the development of a sound research programme to investigate the issue further. This paper surveys the literature on spatial skills and investigates the various underlying cognitive skills involved. It poses a theoretical model for the relationship between computer science ability and spatial skills, exploring ways in which the cognitive processes involved in each overlap, and hence may influence one another. An experiment shows that spatial skills typically increase as the level of academic achievement in computer science increases. Overall, this work provides a substantial foundation for, and encouragement to develop, a major research programme investigating precisely how spatial skills training influences computer science learning, and hence whether computer science education could be significantly improved.

Von Jack Parkinson, Quintin I. Cutts im Konferenz-Band ICER 2018 im Text Investigating the Relationship Between Spatial Skills and Computer Science (2018)

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Personen
KB IB clear
Stephen Cooper, Maya Israni, Anthony Robins, Janet Rountree, Nathan Rountree, Carsten Schulte, Sheryl Sorby, Karen Wang

Aussagen
KB IB clear
Programmieren ist schwierig

Begriffe
KB IB clear
Informatikcomputer science, Informatik-Unterricht (Fachinformatik)Computer Science Education, MINTscience, technology, engineering, mathematics, notional machine, Programmierenprogramming
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Bücher
Jahr  Umschlag Titel Abrufe IBOBKBLB
2003Computer Science Education 2/20031, 1, 3, 4, 2, 2, 6, 2, 2, 8, 12, 81688136
2008ICER 2008 (Michael E. Caspersen, Raymond Lister, Mike Clancy) 2, 1, 1, 2, 2, 3, 1, 7, 1, 1, 2, 623416117
2015ICER 2015 (Brian Dorn, Judy Sheard, Quintin I. Cutts) 1, 3, 3, 5, 1, 3, 11, 3, 1, 3, 2, 8262038176
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Texte
Jahr  Umschlag Titel Abrufe IBOBKBLB
2003    Learning and Teaching Programming (Anthony Robins, Janet Rountree, Nathan Rountree) 13500
2008    Block Model (Carsten Schulte) 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 331326
2015    Spatial Skills Training in Introductory Computing (Stephen Cooper, Karen Wang, Maya Israni, Sheryl Sorby) 1, 1, 2, 1, 1, 1, 2, 4, 1, 1, 1, 211237

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