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모바일 폰에서의 양 손을 이용한 한글 입력 수행도 예측 모델에 대한 연구

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dc.contributor.author이주우-
dc.contributor.author명노해-
dc.date.accessioned2021-09-09T15:44:23Z-
dc.date.available2021-09-09T15:44:23Z-
dc.date.created2021-06-17-
dc.date.issued2008-
dc.identifier.issn1229-1684-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/125287-
dc.description.abstractWith a rapid extension of functions in mobile phones, text input method has become very important for mobile phone users. Previous studies for text input methods were focused on Fitts' law, emphasizing expert's behaviors with one-handed text input method. However, it was observed that 97% of Korean mobile phone users input texts with two-hands. Therefore, this study was designed to develop a prediction model of two-handed Hangul text entry method including novice users as well as experts for mobile phone. For this study, Fitts' law was hypothesized to predict experts' movement time(MT) whereas Hick-Hyman law for visual search time was hypothesized to be added to MT for novices. The results showed that the prediction model was well fitted with the empirical data for both experts and novices with less than 3% error rates. In conclusion, this prediction model of two-handed Hangul text entry including novice users was proven to be a very effective model for modeling two-handed Hangul text input behavior for both experts.-
dc.languageKorean-
dc.language.isoko-
dc.publisher대한인간공학회-
dc.title모바일 폰에서의 양 손을 이용한 한글 입력 수행도 예측 모델에 대한 연구-
dc.title.alternativeTwo - Handed Hangul Input Performance Prediction Model for Mobile Phone-
dc.typeArticle-
dc.contributor.affiliatedAuthor명노해-
dc.identifier.bibliographicCitation대한인간공학회지, v.27, no.4, pp.73 - 83-
dc.relation.isPartOf대한인간공학회지-
dc.citation.title대한인간공학회지-
dc.citation.volume27-
dc.citation.number4-
dc.citation.startPage73-
dc.citation.endPage83-
dc.type.rimsART-
dc.identifier.kciidART001294427-
dc.description.journalClass2-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorHick-Hyman law-
dc.subject.keywordAuthorTwo-handed Hangul input model-
dc.subject.keywordAuthorPerformance prediction model-
dc.subject.keywordAuthorFitts&apos-
dc.subject.keywordAuthorlaw-
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