The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Automated feedback generation for introductory programming assignments
Abstract
We present a new method for automatically providing feedback for introductory programming problems. In order to use this method, we need a reference implementation of the assignment, and an error model consisting of potential corrections to errors that students might make. Using this information, the system automatically derives minimal corrections to student's incorrect solutions, providing them with a measure of exactly how incorrect a given solution was, as well as feedback about what they did wrong.
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Bernstein, M. S., Little, G., Miller, R. C., Hartmann, B., Ackerman, M. S., Karger, D. R., Crowell, D., & Panovich, K. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866078 · Cited by 686
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Metadata
- Authors
- Singh, R., Gulwani, S., & Solar-Lezama, A.
- Venue
- Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation
- Publisher
- ACM
- Published
- 2013-06-16
- Pages
- 15-26
- ISSN
- 1531-7102 (ISSN-L)
- OpenAlex
- W2012312630
- Cited by
- 383
- References
- 1
- FWCI
- 24.98
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Singh, R., Gulwani, S., & Solar-Lezama, A. (2013). Automated feedback generation for introductory programming assignments. Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation. https://doi.org/10.1145/2491956.2462195
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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.
