Automated extraction of revision events from keystroke data
Automated extraction of revision events from keystroke data
复制标题
从击键数据中自动提取修订事件
DOI:
10.1007/s11145-021-10222-w
复制
发表时间:
2021
影响因子:
2.5
通讯作者:
Chukharev-Hudilainen, Evgeny
中科院分区:
文献类型:
--
作者:
Conijn, Rianne;Dux Speltz, Emily;Chukharev-Hudilainen, Evgeny
Revision plays an important role in writing, and as revisions break down the linearity of the writing process, they are crucial in describing writing process dynamics. Keystroke logging and analysis have been used to identify revisions made during writing. Previous approaches include the manual annotation of revisions, building nonlinear S-notations, and the automated extraction of backspace keypresses. However, these approaches are time-intensive, vulnerable to construct, or restricted. Therefore, this article presents a computational approach to the automatic extraction of full revision events from keystroke logs, including both insertions and deletions, as well as the characters typed to replace the deleted text. Within this approach, revision candidates are first automatically extracted, which allows for a simplified manual annotation of revision events. Second, machine learning is used to automatically detect revision events. For this, 7120 revision events were manually annotated in a dataset of keystrokes obtained from 65 students conducting a writing task. The results showed that revision events could be automatically predicted with a relatively high accuracy. In addition, a case study proved that this approach could be easily applied to a new dataset. To conclude, computational approaches can be beneficial in providing automated insights into revisions in writing.
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DOI:
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