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Modeling in HCI

Modeling in HCI. Stuart Card Palo Alto Research Center (PARC) Stanford University, CS376 November 19, 2009. Why Model?. EXAMPLE: POINTING DEVICES. Mouse. Engelbart and English. TRADITIONAL METHOD: EVALUATION. Sun Labs. Engelbart. EXPERIMENT: MICE ARE FASTEST.

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Modeling in HCI

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  1. Modeling in HCI Stuart Card Palo Alto Research Center (PARC) Stanford University, CS376 November 19, 2009 s. card

  2. Why Model? s. card

  3. EXAMPLE: POINTING DEVICES Mouse. Engelbart and English s. card

  4. TRADITIONAL METHOD: EVALUATION Sun Labs s. card

  5. Engelbart s. card

  6. EXPERIMENT: MICE ARE FASTEST s. card

  7. WHY? (ENGINIEERING ANALYSIS) 3 Why these results? Time to position mouse proportional to Fitts’ Index of Difficulty ID. Proportionality constant = 10 bits/sec, same as hand. Therefore speed limit is in the eye-hand system, not the mouse. Therefore, mouse is a near optimal device. Mouse 2 Movement Time (sec) 1 T = 1.03 + .096 log2 (D/S + .5) sec 0 2 1 3 4 5 6 ID=log (Dist/Size + .5) 2 s. card

  8. ENGINEERING ANALYSIS (Modeling) • Insightful • Accumulate into a discipline • Generative s. card

  9. CUMULATING INTO A DISCIPLINEChapanis Report on HF • (National Research Council) • Experimental methods alone are inadequate. • Of 40 non-experimental techniques in human factors, only 2 were validated and taught. s. card

  10. TO BE GENERATIVE • Task analysis • Approximation • Calculation • Zero-parameter predictions s. card

  11. EXAMPLE: ALTERNATIVE DEVICES Headmouse: No chance to win s. card

  12. ATTACHING POINTING DEVICE Use transducer on high bandwidth muscles s. card

  13. EXAMPLE: STRUCTURING THE TASK SPACE BY PROJECTING THE MODEL Word TIME (msec) Period Paragraph Char 500 2000 0 1000 1500 Mouse (Arm) Easy Hard Head- mouse (Head) Hard Easy Fingers Hard s. card

  14. EXAMPLE: BEATING THE MOUSE Use transducer on high bandwidth muscles s. card

  15. DESIGNS FROM RESTRUCTURED TASK SPACE Work with Bill Moggridge, IDEO s. card

  16. EXAMPLE: DESIGN SPACE s. card

  17. MORPHOLOGICAL DESIGN: GENERATINGALL INPUTDEVICES s. card

  18. use and context human computer POINTING DEVICES s. card

  19. MODEL HUMAN PROCESSOR • Processors and Memories applied to human • Used for routine cognitive skill s. card

  20. s. card

  21. EXAMPLE: ZERO-PARAMETER CALC • Problem:Inventor claims he invented 600 wpm typewriter. License and develop? • Solution 1:Half stroke: tM = 70 ms/charWhole stroke: tM + tM = 140 ms/charbut if between hands, overlap: tM = 70 ms = 131 words/min s. card

  22. EXAMPLE: ZERO-PARAMETER CALC • Solution 2: (range calculation)Half stroke:tM=70 [30~100] ms/char = 131 [308~92] words/min • Conclusion: Bogus claim. Throw himout! s. card

  23. task analysis TASK ANALYSIS: GOMS(GOALS, OPERATORS, METHODS, SELECTION RULES) GOAL: EDIT-MANUSCRIPT •repeat until done GOAL: EDIT-UNIT-TASK GOAL: ACQUIRE-UNIT-TASK • if not remembered GET-NEXT-PAGE • if at end of page GET-NEXT-TASK • if an edit task found GOAL: EXECUTE-UNIT-TASK GOAL: LOCATE-LINE • if task not on line [select : USE-QS-METHOD USE-LF-METHOD] GOAL: MODIFY-TEXT [select USE-S-COMMAND USE-M-COMMAND] s. card

  24. PREDICTS TIME WITHIN ABOUT 20% s. card

  25. SAE RECOMMENDED PRACTICE J2365 • Predict task times for car navigation systems • Check compliance with SAE J2364 (15-Second Rule) • Note: To estimate times while driving, multiply by 1.3 to 1.5. • Based on GOMS and work by Paul Green at Univ. of Michigan Transportation Research Institute. Dario Salvucci s. card

  26. SAE J2365 OPERATOR TIMES Paul Green UMITRI s. card

  27. LHX HELICOPTER SIMULATION(Corker, Davis, Papazian, & Pew, 1986) POP-UP-AND-SCAN POP-UP-FOR-SCAN [in parallel-do: LOOK-FOR POP-UP] STABILIZE-CRAFT HOVER-AND-SCAN [in-parallel-do: HOVER SCAN] GOMS used as task analysis to code doctrine s. card

  28. use and context human computer IMMEDIATE BEHAVIOR s. card

  29. HUMAN INFORMATION INTERACTION s. card

  30. GOMS • Routine cognitive skill • Well-known path s. card

  31. Problem solving Heuristic search Exponential if don’t know what to do Information Search s. card

  32. OPTIMALITY THEORY Optimal Foraging Theory Information Foraging Theory Information Energy [ ] [ ] Energy Time Useful info Time Max Max s. card

  33. Information Foraging Theory: People are information rate maximizers of benefits/costs Information has a cost structure s. card

  34. INFORMATION PATCHES e.g. desk piles, Alta vista search list unlike animals foraging for food, humans can do patch construction s. card

  35. CHARNOV’S MARGINAL VALUE THEOREM: max gain when slope of within-path gain g = average gain R (tangent in diagram) Gain R* g(tW) Between-patch time Within-patch time tB t* s. card

  36. BETWEEN-PATCH ENRICHMENT Gain R2 R1 g(tW) Between-patch time Within-patch time tB1 tB2 t2* t1* enrichment Example: arrange physical office efficiently s. card

  37. Gain R2 g2(tW) R1 tB t2* t1* WITHIN-PATCH ENRICHMENT Behavior adapts to cost structure of environment. Example: Better filtering of search hits g1(tW) Between-patch time Within-patch time s. card

  38. WITHIN-PATCH ENRICHMENT:INFORMATION SCENT perception of value and cost of a path to a source based on proximal cues Tokyo New York San Francisco s. card

  39. RELEVANCE-ENHANCED THUMBNAILS • Emphasize text that is relevant to query • Text callouts • Enlarge text that might be helpful in assessing page • Enlarge headers Allison Woodruff s. card

  40. 100 80 Linear Exponential 60 Number of Pages Visited per Level 40 20 0 0 0.05 0.1 0.15 0.2 f PHASE TRANSITION IN NAVIGATION COSTS AS FUNCTION OF INFORMATION SCENT 150 150 Probability of choosing wrong link (f) .150 .150 100 100 Number of pages visited .125 50 50 .100 .100 0 0 0 0 2 2 4 4 6 6 8 8 10 10 Depth Notes: Average branching factor = 10 Depth = 10 s. card

  41. IMPORTANCE FOR WEB DESIGN Jarad Spool, UIE s. card

  42. MACHINE MODELING OF INFORMATION SCENT new cell Information Goal medical patient Link Text treatments dose procedures beam s. card

  43. PREDICTION OF LINK CHOICE 50 35 (b) Yahoo (a) ParcWeb 30 40 25 Predicted frequency 30 20 R2= 0.72 Predicted frequency 15 20 10 R2= 0.90 10 5 0 0 0 10 20 30 40 50 0 5 10 15 20 25 30 35 Observed frequency Observed frequency s. card

  44. Determine relevance of documents Calculate Pr(Link Choice) for each page Examine user patterns Start users at page Flow users through the network .5 .3 .2 USER FLOW MODEL User need (vector of goal concepts) s. card

  45. BLOODHOUND PROJECT INPUT Starting Point: www.xerox.com Task: look for “high end copiers” OUTPUT usability metrics Chi, et al s. card

  46. Information Cost Landscapes Exercise s. card

  47. Gain Overall rate of gain (R) gain(patch foraging time) Time Travel Optimal patch foraging time Moving to a Patch s. card

  48. t1 t2 How long to get to any one itemin a patch? total items in patches Gain n items accessible   Time t2 ave. time for patches time to get one item s. card

  49. Example: Rectangular patch of patches D1 D3 D6 D2 D4 D5 D7 D24 C2 C5 C1 C3 C4 D8 D23 B1 C6 C16 B2 B3 D9 A D22 B8 C7 C15 B4 D10 D21 B7 C8 C14 B6 B5 D11 D20 C12 C9 C13 C11 C10 D12 D19 D17 D14 D18 D16 D15 D13 10 items/patch s. card

  50. Task Names (Patch Names) s. card

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