Understanding the keystroke log: the effect of writing task on keystroke features
Open accessAbstract
Keystroke logging is used to automatically record writers’ unfolding typing process and to get insight into moments when they struggle composing text. However, it is not clear which and how features from the keystroke log map to higher-level cognitive processes, such as planning and revision. This study aims to investigate the sensitivity of frequently used keystroke features across tasks with different cognitive demands. Two keystroke datasets were analyzed: one consisting of a copy task and an email writing task, and one with a larger difference in cognitive demand: a copy task and an academic summary task. The differences across tasks were modeled using Bayesian linear mixed effects models. Posterior distributions were used to compare the strength and direction of the task effects across features and datasets. The results showed that the average of all interkeystroke intervals were found to be stable across tasks. Features related to the time between words and (sub)sentences only differed between the copy and the academic task. Lastly, keystroke features related to the number of words, revisions, and total time, differed across tasks in both datasets. To conclude, our results indicate that the latter features are related to cognitive load or task complexity. In addition, our research shows that keystroke features are sensitive to small differences in the writing tasks at hand.
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Writing behaviours derived from keystroke data provide meaningful insights into a writer's cognitive activities, offering a foundational basis for inferring cognitive processes through keystroke dynamics.
“Writing behaviours derived from keystroke data have been empirically and theoretically shown in previous studies to provide meaningful insights into a writer's cognitive activities, offering a foundational basis for inferring cognitive processes through keystroke dynamics.”
From Ink and algorithm: Exploring temporal dynamics in generative <scp>AI</scp> ‐assisted writing (2026) · cites this paper as Conijn et al., 2019
Conijn et al.'s (2019) findings suggest that keystroke features such as word count and number of revisions are associated with cognitive load.
“For example, Conijn et al.'s (2019) findings suggest that keystroke features such as word count and number of revisions are associated with cognitive load.”
From Ink and algorithm: Exploring temporal dynamics in generative <scp>AI</scp> ‐assisted writing (2026) · cites this paper as Conijn et al., 2019
Conijn et al. (2019) proposed the method of mapping features from the keystroke logs to higher level cognitive processes, such as planning and revising.
“Conijn et al. (2019) proposed the method of mapping features from the keystroke logs to higher level cognitive processes, such as planning and revising.”
From Ink and algorithm: Exploring temporal dynamics in generative <scp>AI</scp> ‐assisted writing (2026) · cites this paper as Conijn et al., 2019
Keystroke data can offer valuable insights into how writers manage and distribute their cognitive resources while composing text.
“Several studies have shown that keystroke data can offer valuable insights into how writers manage and distribute their cognitive resources while composing text (Conijn et al., 2019;Zhang et al., 2021).”
From Ink and algorithm: Exploring temporal dynamics in generative <scp>AI</scp> ‐assisted writing (2026) · cites this paper as Conijn et al., 2019
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K-NN for Hand Keypoints Detection Based on SIBI Standard
Handayani, A. N., Akbar, M., Rosyid, H. A., Arifianto, M., Fukuda, O., & Asmara, R. A. (2024) · International Conference on Electrical and Information Technology (IEIT) · doi:10.1109/ieit64341.2024.10763365
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Rao, B. S. A., Gurram, S., Carvalho, J., S, A. P., & Giri, A. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574328
An Analytical Study of the Report Writing Process in the Classroom
Chandravadiya, D. K., & Mevada, D. S. (2023) · Vidhyayana · doi:10.58213/kzhhe451
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- Authors
- Conijn, R., Roeser, J., & van Zaanen, M.
- Affiliations
- Tilburg University (NL)
- Nottingham Trent University (GB)
- Corresponding author
- Conijn, Rianne
- Roeser, Jens
- Venue
- Reading and Writing
- Published
- 2019-05-15
- Volume
- 32
- Issue
- 9
- Pages
- 2353-2374
- ISSN
- 0922-4777 (ISSN-L)
- 1573-0905
- OpenAlex
- W2945617977
- Cited by
- 55
- References
- 66
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 9 · 2023: 9 · 2022: 11 · 2021: 8 · 2020: 2
- FWCI
- 14.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Reading and Writing (publishedVersion, cc-by)
- Research portal (Tilburg University) (publishedVersion, other-oa)
- Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Social Sciences › Education › Writing and Handwriting Education
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- keystroke logging, cognitive load, cognitive demand, writing process, revision, planning, email writing
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 ›
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