The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
37 Million Compilations: Investigating Novice Programming Mistakes in Large-Scale Student Data
Open accessAbstract
Previous investigations of student errors have typically focused on samples of hundreds of students at individual institutions. This work uses a year's worth of compilation events from over 250,000 students all over the world, taken from the large Blackbox data set. We analyze the frequency, time-to-fix, and spread of errors among users, showing how these factors inter-relate, in addition to their development over the course of the year. These results can inform the design of courses, textbooks and also tools to target the most frequent (or hardest to fix) errors.
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Cites
An Empirical Investigation into Programming Language Syntax
Stefik, A., & Siebert, S. (2013) · ACM Transactions on Computing Education · doi:10.1145/2534973 · Cited by 295
All syntax errors are not equal
Denny, P., Luxton-Reilly, A., & Tempero, E. (2012) · Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education · doi:10.1145/2325296.2325318 · Cited by 144
Investigating novice programming mistakes: educator beliefs vs. student data
Brown, N. C. C., & Altadmri, A. (2014) · Proceedings of the tenth annual conference on International computing education research · doi:10.1145/2632320.2632343 · Cited by 89
Self-citation
Research works printed but not linked: 10
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Cited by
Students’ Misconceptions and Other Difficulties in Introductory Programming: A Literature Review
Qian, Y., & Lehman, J. (2018) · ACM Transactions on Computing Education · doi:10.1145/3077618 · Cited by 529
Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming
Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., & Grossman, T. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580919 · Cited by 366
“One main approach to improving the learning experience is the use of assistive programming environments, which can help alleviate misconceptions in syntax [1, 35], and conceptual knowledge [81].”
Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., et al. (2015) · Proceedings of the 2015 ITiCSE on Working Group Reports · doi:10.1145/2858796.2858798 · Cited by 332
Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests
Hellas, A., Leinonen, J., Sarsa, S., Koutcheme, C., Kujanpää, L., & Sorva, J. (2023) · arXiv · doi:10.48550/arxiv.2306.05715 · Cited by 211
Generating Diverse Code Explanations using the GPT-3 Large Language Model
MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022) · Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2 · doi:10.1145/3501709.3544280 · Cited by 200
Metadata
- Authors
- Altadmri, A., & Brown, N. C. C.
- Venue
- Proceedings of the 46th ACM Technical Symposium on Computer Science Education
- Publisher
- ACM
- Published
- 2015-02-24
- Pages
- 522-527
- OpenAlex
- W1971916043
- Cited by
- 261
- References
- 3
- FWCI
- 16.04
- Type
- conference paper
- Language
- en
- Open access
- bronze
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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.
