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Cited by 289

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Studying the language and structure in non-programmers' solutions to programming problems

Open access
PANE, JOHN F., RATANAMAHATANA, CHOTIRAT “ANN”, MYERS, BRAD A.·2001·International Journal of Human-Computer Studies

Abstract

Programming may be more difficult than necessary because it requires solutions to be expressed in ways that are not familiar or natural for beginners. To identify what is natural, this article examines the ways that non-programmers express solutions to problems that were chosen to be representative of common programming tasks. The vocabulary and structure in these solutions is compared with the vocabulary and structure in modern programming languages, to identify the features and paradigms that seem to match these natural tendencies as well as those that do not. This information can be used by the designers of future programming languages to guide the selection and generation of language features. This design technique can result in languages that are easier to learn and use, because the languages will better match beginners' existing problem-solving abilities.

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Crossref · 10.1006/ijhc.2000.0410

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Metadata

Authors
PANE, J. F., RATANAMAHATANA, C. “., & MYERS, B. A.
Venue
International Journal of Human-Computer Studies
Publisher
Elsevier BV
Published
2001-02
Volume
54
Issue
2
Pages
237-264
ISSN
1071-5819 (ISSN-L)
OpenAlex
W2165605851
Cited by
289
References
3
FWCI
51.47
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Studying the language and structure in non-programmers' solutions to programming problems. https://app.cochat.ai/papers/10.1006/ijhc.2000.0410
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PANE, J. F., RATANAMAHATANA, C. “., & MYERS, B. A. (2001). Studying the language and structure in non-programmers' solutions to programming problems. International Journal of Human-Computer Studies. https://doi.org/10.1006/ijhc.2000.0410

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.