Measuring errors in text entry tasks: an application of the Levenshtein string distance statistic
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Supporting notable information in office work
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Practices for capturing short important thoughts
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Understanding the micronote lifecycle: improving mobile support for informal note taking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Why use memo for all?: restructuring mobile applications to support informal note taking
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Because I carry my cell phone anyway: functional location-based reminder applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Management of personal information scraps
CHI '07 Extended Abstracts on Human Factors in Computing Systems
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Natural language interface for information management on mobile devices
Behaviour & Information Technology
Information scraps: How and why information eludes our personal information management tools
ACM Transactions on Information Systems (TOIS)
An observational study of undergraduate students' adoption of (mobile) note-taking software
Computers in Human Behavior
Managing microfinance with paper, pen and digital slate
Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development
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Despite the potential benefits of digital note taking tools, research has found that people continue to use paper for creating micronotes, informal personal notes such as reminders and to-dos. Design recommendations from formative studies suggest that "natural" input modalities such as voice and digital ink could help to overcome the drawbacks of text entry on phones and PDAs. We conducted an 18-person lab study to understand the perceived and actual trade-offs that these non-traditional input methods offer for micronote capture. We found that people preferred ink (8 participants) and voice (8 participants) input over keyboard (2 participants) input. Half our participants varied the input method they used in different environments, while the rest did not. However, paper remains popular and was preferred by 8 participants when given the option. The 9 participants whose ink and voice micronotes were transcribed with higher error rates had a noticeably different experience using voice including slower capture times, and higher mental and physical demand survey responses. The percentage of participants that preferred ink, voice, and keyboard was the same for both transcription quality groups.