The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
To block or not to block, that is the question: students' perceptions of blocks-based programming
Abstract
Blocks-based programming tools are becoming increasingly common in high-school introductory computer science classes. Such contexts are quite different than the younger audience and informal settings where these tools are more often used. This paper reports findings from a study looking at how high school students view blocks-based programming tools, what they identify as contributing to the perceived ease-of-use of such tools, and what they see as the most salient differences between blocks-based and text-based programming. Students report that numerous factors contribute to making blocks-based programming easy, including the natural language description of blocks, the drag-and-drop composition interaction, and the ease of browsing the language. Students also identify drawbacks to blocks-based programming compared to the conventional text-based approach, including a perceived lack of authenticity and being less powerful. These findings, along with the identified differences between blocks-based and text-based programming, contribute to our understanding of the suitability of using such tools in formal high school settings and can be used to inform the design of new, and revision of existing, introductory programming tools.
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“Viewing this characteristic of blocks-based tools through a distributed cognition lens [17, 21] provides a larger explanation that encompasses many of the features of blocks-based environments that students cited as supporting their learning.”
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Metadata
- Authors
- Weintrop, D., & Wilensky, U.
- Venue
- Proceedings of the 14th International Conference on Interaction Design and Children
- Publisher
- ACM
- Published
- 2015-06-21
- Pages
- 199-208
- OpenAlex
- W2065364955
- Cited by
- 425
- References
- 28
- FWCI
- 23.22
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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