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 requently 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.
- Academic writing
- Bayesian linear mixed effects models
- Keystroke logging
- Typing characteristics
- Writing analytics