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Diagnosing and Coping with Mode Errors in Korean-English Dual-language Keyboard

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Published:02 May 2019Publication History

ABSTRACT

In countries where languages with non-Latin characters are prevalent, people use a keyboard with two language modes namely, the native language and English, and often experience mode errors. To diagnose the mode error problem, we conducted a field study and observed that 78% of the mode errors occurred immediately after application switching. We implemented four methods (Auto-switch, Preview, Smart-toggle, and Preview & Smart-toggle) based on three strategies to deal with the mode error problem and conducted field studies to verify their effectiveness. In the studies considering Korean-English dual input, Auto-switch was ineffective. On the contrary, Preview significantly reduced the mode errors from 75.1% to 41.3%, and Smart-toggle saved typing cost for recovering from mode errors. In Preview & Smart-toggle, Preview reduced mode errors and Smart-toggle handled 86.2% of the mode errors that slipped past Preview. These results suggest that Preview & Smart-toggle is a promising method for preventing mode errors for the Korean-English dual-input environment.

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References

  1. 2017. ATOK. http://www.justsystems.com/jp/products/atok/feature2. html?index=correctionGoogle ScholarGoogle Scholar
  2. 2017. Automatically switch keyboard to match language of surround­ ing text. https://support.office.com/en-us/article/Automatically­ switch-keyboard-to-match-language-of-surrounding-text­ c7316c3a-b6b3--433f-87f4-f777cf13deccGoogle ScholarGoogle Scholar
  3. 2017. KeySwitcher. http://www.keyswitcher.com/Default.aspxGoogle ScholarGoogle Scholar
  4. 2017. Mac OS Input menu. https://support.apple.com/kb/PH22033? viewlocale=en_US&locale=en_USGoogle ScholarGoogle Scholar
  5. 2017. Windows OS Keyboard Layout Language. https: //support.office.com/en-gb/article/Enable-or-change-a-keyboard­ layout-language-1c2242c0-fe15--4bc3--99bc-535de6f4f258Google ScholarGoogle Scholar
  6. Young-Hoon Ahn and Seung-Shik Kang. 2001. Statistical approach to the automatic Korean-English string conversion. In Proceedings of the 13th Annual Conference on Human and Cognitive Language Technology. sigHCLT, Gyeongju, Korea, 205--208.Google ScholarGoogle Scholar
  7. Stephen A Brewster, Peter C Wright, and Alistair DN Edwards. 1994. The design and evaluation of an auditory-enhanced scrollbar. In Pro­ ceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 173--179. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. C. Chawannakul and S. Prasitjutrakul. 2011. Keyboard layout mis­ match error detection and correction system utility. In 2011 8th In­ ternational Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). 488--491.Google ScholarGoogle Scholar
  9. Zheng Chen and Kai-Fu Lee. 2000. A new statistical approach to Chinese Pinyin input. In Proceedings of the 38th annual meeting on association for computational linguistics. Association for Computational Linguistics, 241--247. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Yo Ehara and Kumiko Tanaka-Ishii. 2008. Multilingual Text Entry using Automatic Language Detection. IJCNLP (2008).Google ScholarGoogle Scholar
  11. Y. Ikegami, Y. Sakurai, and S. Tsuruta. 2012. Modeless Japanese Input Method Using Multiple Character Sequence Features. In 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS). 613--618. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Seiji Kasahara, Mamoru Komachi, Masaaki Nagata, and Yuji Mat­ sumoto. 2011. Error Correcting Romaji-kana Conversion for Japanese Language Education. Proceedings of the Workshop on Advances in Text Input Methods (2011), 38--42.Google ScholarGoogle Scholar
  13. Injeong Kim and Heeyeung Hwang. 1989. A study on the Hangul automata. The Transactions of the Korean Institute of electrical engineers (1989), 491--494.Google ScholarGoogle Scholar
  14. Keung-Hae Lee. 1995. Perfonnance Evaluation of AIMS, an Automatic Korean / English Input Mode Switching System. In Proceedings of the Korean Information Science Society Conference.Google ScholarGoogle Scholar
  15. Keung-Hae Lee. 1997. The Application Programming Interface of AIMS, A Predictive Input Mode Switching Technique. In Proceedings of the Korean Information Science Society Conference. kiise, Busan, Korea, 185--188.Google ScholarGoogle Scholar
  16. Ken Lunde. 2008. Input Methods. In CJKV Information Processing: Chinese, Japanese, Korean, and Vietnamese Computing. "O'Reilly Media, Inc.", Chapter 5, 299--362.Google ScholarGoogle Scholar
  17. Seung mok Yoo, Jeong hoon Lee, and Sam myo Kim. 1995. Automatic Korean / English Key Mode Converter. In Proceedings of the Korean Information Science Society Conference. 589--592.Google ScholarGoogle Scholar
  18. Andrew Monk. 1986. Mode errors: a user-centred analysis and some preventative measures using keying-contingent sound. International Journal of Man-Machine Studies 24, 4 (April 1986), 313--327. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Donald A. Norman. 1983. Design Rules Based on Analyses of Human Error. Commun. ACM 26, 4 (April 1983), 254--258. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Merle F Poller and Susan K Garter. 1984. The effects of modes on text editing by experienced editor users. Human Factors 26, 4 (1984), 449--462.Google ScholarGoogle ScholarCross RefCross Ref
  21. Tanapong Potipiti, Virach Sornlertlamvanich, and Kanokwut Thanad­ kran. 2001. Towards an Intelligent Multilingual Keyboard System. In Proceedings of the First International Conference on Human Language Technology Research (HLT '01). Association for Computational Lin­ guistics, Stroudsburg, PA, USA, 1--4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Abigail J. Sellen, Gordon P. Kurtenbach, and William A. S. Buxton. 1992. The Prevention of Mode Errors Through Sensory Feedback. Hum.-Comput. Interact. 7, 2 (June 1992), 141--164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Ben Shneiderman, Catherine Plaisant, Maxine S Cohen, Steven Jacobs, Niklas Elmqvist, and Nicholas Diakopoulos. 2016. Designing the user interface: strategies for effective human-computer interaction. Pearson. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Diagnosing and Coping with Mode Errors in Korean-English Dual-language Keyboard

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        cover image ACM Conferences
        CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
        May 2019
        9077 pages
        ISBN:9781450359702
        DOI:10.1145/3290605

        Copyright © 2019 ACM

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        Publication History

        • Published: 2 May 2019

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        CHI '19 Paper Acceptance Rate703of2,958submissions,24%Overall Acceptance Rate6,199of26,314submissions,24%

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