The GOMS Family of User Interface Analysis Techniques : Comparison and Contrast

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Korea University Dept.of Industrial System & Information Engineering User Interface Lab The GOMS Family of User Interface Analysis Techniques : Comparison and Contrast + 1 조

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The GOMS Family of User Interface Analysis Techniques : Comparison and Contrast. + 1 조. 2. 3 Natural GOMS Language (NGOMSL). - a structured natural-language notation for representing GOMS models and a procedure for constructing them. - PowerPoint PPT Presentation

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Page 1: The GOMS Family of User Interface Analysis Techniques :  Comparison and Contrast

Korea University Dept.of Industrial System & Information Engineering User Interface Lab

The GOMS Family of User InterfaceAnalysis Techniques : Comparison and Contrast

+ 1조

Page 2: The GOMS Family of User Interface Analysis Techniques :  Comparison and Contrast

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2. 3 Natural GOMS Language (NGOMSL)2. 3 Natural GOMS Language (NGOMSL)

- a structured natural-language notation for representing GOMS models and a procedure for constructing them.

- NGOMSL : program form, goal structure 표현 (operator sequence 예측 → execution time, learning time, transfer of procedure learning)

2. 3. 1 - NGOMSL 의 basic Goms concept → Cognitive Complexity Theory (CCT) : CCT ? simple serial-stage architecture 로서 working memory 에서 production rules 를 활성화 . 이런 rules 는 working memory 의 contents 를 변경하거나 primitive external operators (such as key stroke) 를 실행 . 따라서 execution time, learning time, transfer of procedure learning 의 예측이 가능 ) 하지만 , CCT 의 특성상 NGOMSL 은 how perceptual, cognitive and motor might overlap 을 표현 못함 . (CPM-GOMS)

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2. 3 Natural GOMS Language (NGOMSL)2. 3 Natural GOMS Language (NGOMSL)

2. 3. 2 - NGOMSL 은 execution time 예측 뿐 아니라 learning time 을 예측 . 즉 , 정량적으로 KLM 과 CMS-GOMS 가 제공하는 것 이상으로 제공 . Working memory 와 long-term memory 사용을 명확히 표현 .

- Learning Time Predictions 1)NGOMSL 은 system 사용에 걸리는 시간 즉 , Learning time 예측에 있어 효과적이다 . 이것은 사용자가 이미 operators 를 실행하는 방법을 안다는 가정하에서다 . pure learning time → GOMS 는 operators, 그 자체를 실행하는 실행하는 관련된 knowledge 를 표현하는 것이 아니라 goal 을 달성하기 위해 필요한 operators 의 knowledge 를 표현한다 .

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2. 3 Natural GOMS Language (NGOMSL)2. 3 Natural GOMS Language (NGOMSL)

- Execution Time Predictions 1) NGOMSL 은 다른 GOMS 모델과 같이 execution time 예측이 the sequence of operators 에 기초 Execution time 예측 : 각 NGOMSL Statement 당 0.1 초씩 count + total external operator time based on the KLM EX) 16.38 : KLM 과 CMN – GOMS 에서는 14.38 2. 3. 3 Comparison with KLM and CMN-GOMS - execution time 의 주요 차이는 cognitive and perceptual operators 에 어떻게 시간을 배정하는가 . - 관측이 불가능한 operators 의 특성 KLM : single crude M operator 는 각 cognitive unit 앞에 CMN-GOMS : cognitive unit 뒤에 NGOM 니 : CCT 를 기초로 했기 때문에 각 단계에서 앞 , 뒤에 규칙적인 cognition execution time 필요

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2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)

- CPM-GOMS model 또한 다른 모델처럼 component activities 분석에 기초해서 execution time 예측 분석의 수준이 primitive operators 가 simple perceptual, cognitive, motor acts 까지 요구 한다 . 또한 Serial 이 아닌 task 에 따라 parallel performance 로 분석이 가능 .

※ CPM : 1) Cognitive-Perceptual-motor 2) the Critical-Path-Method (provide the prediction of total task time)

2. 4. 1 Architectural Basis and Constraints - CPM-GOMS is based directly on the Model Human Processor (MHP) Human is modeled by Processors and Storage system 1) Sensory information 이 먼저 acquired, recognized 2) 이것들이 perceptual processors 에 의해 working memory 에 저장 3) Cognitive processor 가 physical action 을 하도록 information and commands motor processor 작동 ※ 각 processor 는 내부적으로 직렬적으로 operate 되지만 외부적으로는 병렬적으로 run 된다 .

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2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)

- MHP architecture 는 CPM-GOMS 가 parallelism 을 적용할 수 있게 해준다 .

- CPM-GOMS model 은 사용자가 task 에 대해서 숙련되어 있다고 가정 .

2. 4. 2 Example CPM-GOMS model - CPM-GOMS 는 모델 구축이 CMN-GOMS model 에서 시작 - cognitive, perceptual, motor operators, dependencies 로 구성된 Template ( By John and Gray (1995))

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2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)

- Template chart 에서 가장 긴 Path 는 critical path 라고 불리는 operators 의 sequence 로서 이를 기초로 task 의 total duration 을 추정할 수 있다 . 즉 , 정량적인 performance time 의 예측은 CPM-GOMS model 의 critical path 로부터 결정된다 .

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2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)

- Execution Time Prediction 1) Box 의 시간은 Jhon and Gray(1995) 의 durations 추정에 기초 2) Critical Path = total execution time = 2.21 seconds 3) 앞의 그림에서 볼 수 있듯이 move-to-beginning-of-phrase 에서 병렬 진행 즉 , 새로운 location 으로 cursor 가 이동하면서 eye movement 와 perception of information 이 병렬적 진행 → 커서가 새로운 position 에 위치했는지 확인 전에 눈은 이미 이동 , 하지만 mouse movements 는 eye movement 보다 오래 걸림 : critical path 생성 4) 복잡한 task (shift – click – mouse – button) : 오른손으로 mouse 이동 , 왼손으로 shift key 누르기

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2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)2. 4 Cognitive – Perceptual – Motor GOMS (CPM-GOMS)

2. 4. 3 Comparison with KLM, CMN-GOMS and NGOM 니 - CPM-GOMS 는 CMN-GOMS 모델에서 시작되었기 때문에 directing mapping 가능 - KLM 에서는 selection rule 이 있지만 CPM 에서는 없다 (because CPM 은 sequence form 으로 구성 )

- CPM 모델이 reasonable 함에도 불구하고 정량적 prediction 이 다른 모델보다 짧은 이유는 다른 GOMS 모델과 다르게 CPM 은 숙련된 사용자를 가정 ( 빠른 MHP architecture 가정 ) ※ 숙련된 telephone operators handling calls 에 효과적 1) 숙련자는 phrase 를 옮기기 위해 어디를 봐야 하는지 정확히 알고 있다 . 2) method 나 복잡한 결정을 위한 selection 에 관련된 cognitive activity 가 없다 . ( 다른 GOMS 에서 M-like 로 표현 , CPM 에서는 working memory, 기억과 MHP 에 의해 minimum cognitive activity 로 표현 )

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3. Summary and Comparison of the GOMS Techniques3. Summary and Comparison of the GOMS Techniques

3. 1 Summary Comparison of Predictions

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3. Summary and Comparison of the GOMS Techniques3. Summary and Comparison of the GOMS Techniques

3. 2 Summary Comparison of Operation Times

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3. Summary and Comparison of the GOMS Techniques3. Summary and Comparison of the GOMS Techniques

3. 3 Summary Comparison of Architectural activity 적용

- KLM : 기본적 cognitive architecture ( 적용이 쉽다 . Execution time 만을 예측 )

- CNM-GOMS : 좀더 복잡한 cognitive architecture ( 모든 일반적인 task instances 에 적용하며 execution time 예측 )

- NGOMSL : procedure knowledge 와 working memory 에 기초한 sequential architecture ( Learning time 예측 가능 )

- CPM-GOMS : powerful 하지만 비교적 unspecified multiple parallel processor architecture ( execution time 예측이 정교 . Activities 의 overlapping pattern 에 적용가능 (parallel))

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4. Conclusion4. Conclusion

- GOMS model 은 goal 수행을 위한 procedures 의 중요성을 강조 함으로서 human-computer interaction 의 과학적 이론일 뿐 아니라 practical design 을 위한 유용한 tool 이다 . Current GOMS 모델은 Procedural speed 와 complexity 를 반영하여 매우 효과적이지만 interface 와 human performance 의 다른 측면 (perceptual – cognitive – motor interactions, comprehension processes, problem-solving, learning mechanism) 에 대한 연구를 진행하여 더욱 정확하고 효과적인

tool 이 될 것이다 .