Investigating novice programming mistakes: educator beliefs vs. student data
Abstract
Educators often form opinions on which programming mistakes novices make most often - for example, in Java: "they always confuse equality with assignment", or "they always call methods with the wrong types". These opinions are generally based solely on personal experience. We report a study to determine if programming educators form a consensus about which Java programming mistakes are the most common. We used the Blackbox data set to check whether the educators' opinions matched data from over 100,000 students - and checked whether this agreement was mediated by educators' experience. We found that educators formed only a weak consensus about which mistakes are most frequent, that their rankings bore only a moderate correspondence to the students in the Blackbox data, and that educators' experience had no effect on this level of agreement. These results raise questions about claims educators make regarding which errors students are most likely to commit.
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Prior research such as the reported mismatch between educators' beliefs about students' mistakes and logged data about mistakes might be explained in part by students asking for help from educators only when they really need help, which might differ from how they employ automated systems.
“prior research such as the reported mismatch between educators' beliefs about students' mistakes and logged data about mistakes [9] might be explained in part by students asking for help from educators only when they really need help, which might differ from how they employ automated systems.”
From Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests (2023) · cites this paper as [9]
Cites
Statistical Methods for Psychology.
Gudgeon, A. C., & Howell, D. C. (1994) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348956 · Cited by 8,277 · see citing works
Kendall, M. G. (1957) · Biometrika · doi:10.2307/2333282 · Cited by 6,444 · see citing works
Moran, P. A. P., & Kendall, M. G. (1951) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2980903 · Cited by 2,736 · see citing works
Sadler, P., Sonnert, G., Coyle, H. P., Cook-Smith, N., & Miller, J. L. (2013) · American Educational Research Journal · doi:10.3102/0002831213477680 · Cited by 413 · see citing works
Novice mistakes: are the folk wisdoms correct?
Spohrer, J., & Soloway, E. M. (1986) · Communications of the ACM · doi:10.1145/6138.6145 · Cited by 310 · see citing works
Methods and tools for exploring novice compilation behaviour
Jadud, M. C. (2006) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/1151588.1151600 · Cited by 297 · see citing works
An Empirical Investigation into Programming Language Syntax
Stefik, A., & Siebert, S. (2013) · ACM Transactions on Computing Education · doi:10.1145/2534973 · Cited by 295 · see citing works
Identifying and correcting Java programming errors for introductory computer science students
Hristova, M., Misra, A., Rutter, M., & Mercuri, R. T. (2003) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/611892.611956 · Cited by 201 · see citing works
An analysis of patterns of debugging among novice computer science students
Ahmadzadeh, M., Elliman, D., & Higgins, C. (2005) · Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/1067445.1067472 · Cited by 185 · see citing works
Brown, N. C. C., Kölling, M., McCall, D., & Utting, I. (2014) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2538862.2538924 · Cited by 179 · see citing works
Self-citationAll 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 · see citing works
The Average Spearman Rank Correlation Coefficient
Lyerly, S. B. (1952) · Psychometrika · doi:10.1007/bf02288917 · Cited by 101 · see citing works
Identifying Top Java Errors for Novice Programmers
Jackson, J., Cobb, M. L., & Carver, C. A. (2006) · 2022 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2005.1611967 · Cited by 100 · see citing works
Predicting at-risk novice Java programmers through the analysis of online protocols
Tabanao, E. S., Rodrigo, M. M. T., & Jadud, M. C. (2011) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2016911.2016930 · Cited by 91 · see citing works
Teach yourself Java 1.1 in days
Lemay, L., & Perkins, C. (1997) · CERN Document Server (European Organization for Nuclear Research) · Cited by 62 · see citing works
A detector for non-literal Java errors
Dy, T. T., & Rodrigo, M. M. T. (2010) · Koli Calling International Conference on Computing Education Research · doi:10.1145/1930464.1930485 · Cited by 28 · see citing works
Taylor, W. L., & Fong, C. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500885 · Cited by 11 · see citing works
Kolikant, Y. B. (2010) · Instructional Science · doi:10.1007/s11251-010-9140-7 · Cited by 10 · see citing works
Correcting the Average Rank Correlation Coefficient for Ties in Rankings
Taylor, W. L. (1964) · Journal of the American Statistical Association · doi:10.1080/01621459.1964.10480734 · Cited by 9 · see citing works
Hoisington, C. (2012) · Medical Entomology and Zoology · Cited by 2 · see citing works
Java in 24 Hours, Sams Teach Yourself (Covering Java 8)
Cadenhead, R. (2014) · Cited by 2 · see citing works
Flagged first, then by citations
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Qian, Y., & Lehman, J. (2018) · ACM Transactions on Computing Education · doi:10.1145/3077618 · Cited by 529 · see citing works
Introductory programming: a systematic literature review
Luxton-Reilly, A., Simon, Al-Bluwi, I., Becker, B. A., Giannakos, M. N., Kumar, A. N., Ott, L. M., Paterson, J. H., Scott, M., Sheard, J., et al. (2018) · doi:10.1145/3293881.3295779 · Cited by 511 · see citing works
Educational Data Mining and Learning Analytics in Programming
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., et al. (2015) · doi:10.1145/2858796.2858798 · Cited by 332 · see citing works
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Santos, E. A., Campbell, J. C., Patel, D., Hindle, A., & Amaral, J. N. (2018) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner.2018.8330219 · Cited by 85 · see citing works
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Deitrick, E., Shapiro, R. B., Ahrens, M., Fiebrink, R. A., Lehrman, P. D., & Farooq, S. (2015) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2787622.2787715 · Cited by 67 · see citing works
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Mehmood, E., Abid, A., Farooq, M. S., & Nawaz, N. A. H. (2020) · IEEE Access · doi:10.1109/access.2020.3008321 · Cited by 66 · see citing works
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Price, T., Brown, N. C. C., Lipovac, D., Barnes, T. M., & Kölling, M. (2016) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2960310.2960319 · Cited by 36 · see citing works
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- Authors
- Brown, N. C. C., & Altadmri, A.
- Affiliations
- University of Kent (GB)
- Venue
- Proceedings of the tenth annual conference on International computing education research
- Publisher
- ACM
- Published
- 2014-07-28
- Pages
- 43-50
- OpenAlex
- W2013831574
- Cited by
- 89
- References
- 22
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 8 · 2023: 6 · 2022: 9 · 2021: 4 · 2020: 10 · 2019: 5 · 2018: 9 · 2017: 9 · 2016: 7 · 2015: 10 · 2014: 2
- FWCI
- 5.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on International Computing Education Research (ICER) (publishedVersion)
- Kent Academic Repository (University of Kent) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- coding errors, Java, programmer experience, error rate, programming education, method calls
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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