CodeProcess Charts: Visualizing the Process of Writing Code
Open accessAbstract
Instructors of computer programming courses evaluate student progress on code submissions, exams, and other activities. The evaluation of code submissions is typically a summative assessment that gives very little insight into the process the student used when designing and writing the code. Thus, a tool that offers instructors a view into how students actually write their code could have broad impacts on assessment, intervention, instructional design, and plagiarism detection. In this article we propose an interactive software tool with a novel visualization that includes both static and dynamic views of the process that students take to complete computer programming assignments. We report results of an exploratory think-aloud study in which instructors offer thoughts as to the utility and potential of the tool. In the think-aloud study, we observed that the instructors easily identified multiple coding strategies (or the lack of thereof), were able to recognize plagiarism, and noticed a clear need for wider dissemination of tools for visualizing the programming process.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some work has used fine-grained programming process logs from instrumented IDEs to uncover student work patterns and to visualize student programming processes.
“Some work has looked into using fine-grained programming process logs gathered from instrumented IDEs to uncover student work patterns [21-23, 26, 40] and to visualize student programming processes [10,14,35,38].”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [35]
Cites
Schleimer, S., S, W. D., & Aiken, A. (2003) · Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data · doi:10.1145/872757.872770 · Cited by 1,099 · cited 1× · see citing works
“Plagiarism detection methods like MOSS [37] flag submissions for a potential plagiarism based on analysis of only the final code snapshot.”
MethodMorning or Evening? An Examination of Circadian Rhythms of CS1 Students
Zavgorodniaia, A., Shrestha, R., Leinonen, J., Hellas, A., & Edwards, J. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) · doi:10.1109/icse-seet52601.2021.00036 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study
Sheard, J., Denny, P., Hellas, A., Leinonen, J., Malmi, L., & Simon (2024) · Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3626252.3630880 · Cited by 54 · see citing works
Self-citationTowards Giving Timely Formative Feedback and Hints to Novice Programmers
Jeuring, J., Keuning, H., Marwan, S., Bouvier, D., Izu, C., Kiesler, N., Lehtinen, T., Lohr, D., Peterson, A., & Sarsa, S. (2022) · Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3571785.3574124 · Cited by 40 · see citing works
Developing Novice Programmers’ Self-Regulation Skills with Code Replays
Xie, B., Lim, J. O., Pham, P. K. D., Li, M., & Ko, A. J. (2023) · Proceedings of the 2023 ACM Conference on International Computing Education Research V.1 · doi:10.1145/3568813.3600127 · Cited by 18 · see citing works
Lehtinen, T., Haaranen, L., & Leinonen, J. (2023) · Proceedings of the 25th Australasian Computing Education Conference · doi:10.1145/3576123.3576129 · Cited by 17 · see citing works
Self-citation
- Authors
- Shrestha, R., Leinonen, J., Hellas, A., Ihantola, P., & Edwards, J.
- Affiliations
- Utah State University (US)
- University of Helsinki (FI)
- Aalto University (FI)
- Venue
- Proceedings of the 24th Australasian Computing Education Conference
- Publisher
- ACM
- Published
- 2022-02-14
- Pages
- 46-55
- OpenAlex
- W4211085087
- Cited by
- 13
- References
- 2
- Citations by year
- 2025: 6 · 2024: 2 · 2023: 2 · 2022: 3
- FWCI
- 4.24
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Australasian Computing Education Conference (publishedVersion)
- Työväentutkimus Vuosikirja (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- programming exercises, instructor evaluation, plagiarism detection, coding strategies, interactive visualization tool, programming education, think-aloud study
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
