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

1. The GOMS Family of User Interface Analysis Techniques : Comparison and Contrast Bonnie E. John David E. Kieras Young-joo Jeon

2. Abstract Introduction Comparison (1.2.3.4.) Summary and Comparison (1.2.3.) Conclusion Contents

3. Abstact The GOMS model has been one of the most widely known theoretical concepts in HCI. (Since the publication of The Psychology of HCI’) This article compares and contrasts GOMS’s 4 family. (KLM, CMN-GOMS, NGOMSL, CPM-GOMS)

4. Introduction 1.1. The Example Task

5. Introduction Goals - what the user intends to accomplish Operators - actions that are performed to get to the goal Methods - sequences of operators that accomplish a goal Selection Rules - used to describe when a user would select a certain method over the others. Selection rules are often ignored in typical GOMS analyses. Goals vs. Operators 1.2. Definitions of 4-GOMS

6. Introduction Program Form (e.g., mark-and-delete method) 1.3. Form of aGOMS Model Sequence Form (e.g., delete-characters method)

7. Comparison GOMS중 가장 간단한 기술. Execution time예측. (미리 정의된 행동양식의 예상시간을 비교하여 분석하는 방법.) e.g.) K- press a key or button P- point with a mouse to a target on a display H- home hands on the keyboard or other device D- draw a line segment on a grid M- mentally prepare to do an action or a closely related series of primitive actions R- the system response time (user waiting time for the system) 2.1. The Keystroke-Level Model Architectural Basis and Constraints. Simple cognitive architecture: HIP (Human Information Processing)의 Serial stage model 에 기반.

8. Comparison Example KLM. 2.1. The Keystroke-Level Model

9. Comparison Card, Moran, and Newell GOMS. Methods - Program form 로 표현. (submethods, conditionals 포함) Operator sequence, execution time예측 2.2. CMN-GOMS Architectural Basis and Constraints. MHP (Model Human Processor) –parallel-stage architecture 와 HIP (Human Information Processing) 의 Simple conventional model 기반.

10. Comparison Example CMN-GOMS 2.1. The Keystroke-Level Model

11. Comparison Comparison to the KLM. 2.2. CMN-GOMS

12. Comparison Natural GOMS Language Program form로 표현. operator sequence, execution time, learning time예측 2.3. NGOMSL Architectural Basis and Constraints. CCT (Cognitive Complexity Theory)-simple serial-stage architecture working memory에서 production rules를 활성화.

13. Comparison Example NGOMSL - Learning Time Predictions - Execution Time Predictions 2.3. NGOMSL

14. Comparison Comparison with KLM and CMN-GOMS 2.3. NGOMSL

15. Comparison Cognitive-Perceptual-Motor GOMS Critical-Path-Method: (provide the prediction of total task time) execution time 예측 - component activities 분석에 기초 - requirement of analysis level (primitive operator) : simple perceptual, cognitive, motor acts. 2.4. CPM-GOMS

16. Comparison Architectural Basis and Constraints. MHP (Model Human Processor)에 기반. (parallel) Human Perceptual Processors Cognitive Processors Sensory information 1st acquired recognized deposited Physical action Each processor : internal - serially operation, external - parallel running 2.4. CPM-GOMS Extreme expert user

17. Comparison Example CPM-GOMS - Begins with CMN-GOMS model 2.4. CPM-GOMS

18. Comparison 2.4. CPM-GOMS Example CPM-GOMS - Execution Time Predictions

19. Comparison Comparison with KLM, CMN-GOMS, and NGOMSL 2.4. CPM-GOMS

20. Summary and Comparison 3.1. Predictions

21. Summary and Comparison 3.2. Operator Time

22. Summary and Comparison 3.3. Architecture Assumptions Simplest cognitive architecture KLM Easy to apply, Predicts only execution time More complicated cognitive architecture CNM-GOMS Predicts execution time for all subsumed task instances Elaborated sequential architecture Working memory and specified procedure knowledge Explicit representation of procedural knowledge Predict Learning time NGOMSL Predicts subtle execution time, overlapping patterns of activities CPM-GOMS Powerful, unspecified multiple parallel processor architecture

23. Conclusions Importance of the procedures for accomplishing goals (user must learn and follow system) -quantitative predictions of procedure learning and execution time. qualitative insights into the implications of design features. Useful tools for HCI and practical design Expect to improved HCI models (more comprehensive, accurate, and useful)