Spreadsheet Comprehension: Guesswork, Giving Up and Going Back to the Author
Abstract
Spreadsheet users routinely read, and misread, others' spreadsheets, but literature offers only a high-level understanding of users’ comprehension behaviors. This limits our ability to support millions of users in spreadsheet comprehension activities. Therefore, we conducted a think-aloud study of 15 spreadsheet users who read others’ spreadsheets as part of their work. With qualitative coding of participants’ comprehension needs, strategies and difficulties at 20-second granularity, our study provides the most detailed understanding of spreadsheet comprehension to date.
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The researchers chose not to segment videos into predefined episodes or fixed time intervals.
“Thus we chose not to segment videos into predefined episodes or fixed time intervals (e.g., 20-second segments, as exemplified in Srinivasa Ragavan et al. (2021)).”
From Vibe coding: programming through conversation with artificial intelligence (2025) · cites this paper as Srinivasa Ragavan et al. (2021)
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Introduction to understanding people
Hornbæk, K., Kristensson, P. O., & Oulasvirta, A. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192864543.003.0002
Hornbæk, K., Kristensson, P. O., & Oulasvirta, A. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192864543.003.0025
Hornbæk, K., Kristensson, P. O., & Oulasvirta, A. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192864543.003.0012
Hornbæk, K., Kristensson, P. O., & Oulasvirta, A. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780192864543.003.0021
Investigating Information Needs During Spreadsheet Data Analysis
Maruta, A., Tagai, N., & Kato, M. P. (2025) · Journal of Information Processing · doi:10.2197/ipsjjip.33.507
The Invisible Mentor: Inferring User Actions from Screen Recordings to Recommend Better Workflows
Yan, L., Head, A., Milne, K., Le, V., Gulwani, S., Parnin, C., & Murphy-Hill, E. R. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790294
Identifying beacons in Grasshopper: Revealing visual clues to support program comprehension
Haddur, A., Çekmiş, A., Kuyucu, M., & İnce, G. (2026) · International Journal of Architectural Computing · doi:10.1177/14780771261499582
- Authors
- Srinivasa Ragavan, S., Sarkar, A., & Gordon, A. D.
- Affiliations
- Microsoft Research, United Kingdom
- Microsoft Research (United Kingdom) (GB)
- Venue
- Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2021-05-06
- Pages
- 1-21
- OpenAlex
- W3111869873
- Cited by
- 67
- References
- 60
- Citations by year
- 2026: 2 · 2025: 52 · 2024: 5 · 2023: 4 · 2022: 3 · 2021: 1
- FWCI
- 2.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Software › Spreadsheets and End-User Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- comprehension strategies, think-aloud
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.
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