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研究生:劉國常
研究生(外文):Liou Gwo Charng
論文名稱:應用GOMS於中文打字之績效預測
論文名稱(外文):An Exploratory Study of Cognition Involved in Chinese(Kanji) Data Entry Based on GOMS
指導教授:郭峰淵郭峰淵引用關係
指導教授(外文):Kuo Feng Yang
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:1993
畢業學年度:81
語文別:中文
論文頁數:55
中文關鍵詞:人機互動認知模式人員績效
外文關鍵詞:Human-Computer Interaction(HCI)Cognitive ModelGOMS
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由於中文文字的圖形特性,使得鍵盤式的中文輸入並非是最佳的人機溝通
方式,但它卻是目前最普遍的,是以有必要瞭解這項人機互動的行為。在
人機互動中,認知模式可幫助系統設計者瞭解人機介面設計對使用者的影
響,因此也成為人機介面評估、設計和人員訓練的輔助工具。基於認知模
式的重要性,本研究應用Card, et al. 於 1983 年所提出的 GOMS(
Goals, Operators, Methods and Selection rules) 模式來發展中文輸
入作業的認知模式,並以此描述為基礎來預測專家(expert)輸入中文的時
間。為了驗證發展的模式之準確性,我們並設計了一個打字實驗。實驗的
分析乃針對個別受試者,預測其執行特定打字作業的時間。實驗結果顯示
,中文打字時的各項活動(如知覺、認知、運動)有相當的複雜性;但是
本實驗所驗證的模式,仍能成功地預測專家執行打字作業的時間。

One critical issue in adopting computers in Taiwan is to design
interfaces that enable users to easily input Chinese characters(
Kanji). Kanji is inherently different from English. To input
Kanji, a user must decompose a word into radicals, and input
them through a traditional English keyboard on which the
radicals are marked. It appears that such Kanji entry involves
susbstantial cognitive(e.g., momory, motor, and perceptual)
effort. Previously, Card et al. have proposed GOMS(Goals,
Operators, Methods and Selection rules) to model cognition
involved in English data entry. GOMS is a way of describing
what the user needs to know and to do in order to perform
computer-based tasks. The METT(GOMS Model of Expert
Transcription Typing), an extension of GOMS for modeling
nonsequential component processes, has been shown useful for
predicting the time to execute English typing tasks. The
purpose of this thesis is to study if METT can be applied to
predict the performance of Kanji entry. Through such analysis,
we hope to understand cognition involved in Kanji entry. An
experiment was performed to validate the applicability of METT
to model Kanji data entry behavior model. The research findings
indicate that METT can be used to predict performance with
acceptable accuracy. In addition, based on the research
finding, a new model, MECT (GOMS Model of Expert Chinese
Typing), is proposed. A preliminary analysis shows that MECT
can predict performance with better accuracy than METT.
However, this difference between two model's predicting power
is not indicative of their respective theoretical soundness.
Instead, it shows that more studies are needed to understand
the complexity of cognition involved in Kanji entry.

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