Heavily Used Interfaces: Designing for Efficiency


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Full presentation with audio can be accessed on vimeo: http://vimeo.com/36101751

Everyone agrees that interfaces should be easy to use. Some suggest that people should be able to walk up to a new interface and use it without error the first time. "No errors during the first use" is a good ideal for occasionally-used web sites; it is not particularly relevant to back-office applications that are used many hours each day.

Consider the person in a call center that is taking orders or answering questions about a person's account. During each call, the call agent does two things at once: carries on a conversation with the customer and uses software to retrieve, edit, or enter information about an order or account. For the call agent, "easy to use" means "fast to use" - otherwise, the agent won't be able to keep up with the customer's conversation and requests.

Lead by Rick Omanson, Director at User Centric, this webinar will discuss how to design a heavily-used interface to be efficient and "easy-to-use" for expert users.

In this 60-minute webinar, Rick will describe:
• How expert users view, interpret, and use interfaces differently from novice users
• Strengths and weaknesses expert users have in comparison to novice users
• Interface features and strategies that make expert users more efficient and compensate for their weaknesses

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Heavily Used Interfaces: Designing for Efficiency

  1. 1. Heavily-Used Interfaces: Designing forEfficiency Presenter: Rick Omanson Director User Centric, Inc. Moderator: Pamela Stoffregen-Gay Sr. Marketing Manager User Centric, Inc. 1
  2. 2. Some Interfaces are Used Occasionally Information Sites Retail Sites Kiosks Event-Oriented Apps Heavily-Used Interfaces 2
  3. 3. Other Interfaces are Used A Lot Back-Office Operations Electronic Health Records Call Center Apps Gaming Heavily-Used Interfaces 3
  4. 4. Overview How repeated use affects users How repeated use affects usability How repeated use affects design – Designing for efficiency – Designing for skill development Heavily-Used Interfaces 4
  5. 5. How Repeated Use Affects Users Heavily-Used Interfaces 5
  6. 6. Hypothetical Example Old interface – upgrading back end Used everyday, all day long Update it to improve performance Heavily-Used Interfaces 6
  7. 7. “Old” Ordering Screen Heavily-Used Interfaces 7
  8. 8. The Best There is Why efficient? – Everything on page – Direct entry Why isn’t it confusing? – Practice Effects Heavily-Used Interfaces 8
  9. 9. Practice Effects Initial performance is difficult and effortful As behavior repeated, performance improves Improvement called the learning curve (Ebbinghaus, 1885) Heavily-Used Interfaces 9
  10. 10. Learning Curve - Errors Heavily-Used Interfaces 10
  11. 11. Learning Curve - Time Heavily-Used Interfaces 11
  12. 12. Effects of Practice – A Deeper Look Practice results in faster, more accurate performance What designs maximize performance (not learning)? What changes in users when they improve? With repeated use, users: – Remember instead of look – See and remember patterns instead of features – Automate responses Heavily-Used Interfaces 12
  13. 13. Effects of Practice – A Deeper Look – Remember instead of look – See and remember patterns instead of features – Automate responses Heavily-Used Interfaces 13
  14. 14. Learning a Menu System On new pages, menus are scanned one at a time. Looking is a relatively slow, linear process. As the interface is learned, stop scanning and instead recall item location. Recall is a fast, logarithmic process that follows the Hick-Hyman law. (Hick, 1952; Hyman, 1953) Heavily-Used Interfaces 14
  15. 15. Modeling Shift from Looking to Remembering Model assumes people start out always looking With practice, some scanning is replaced by recall With more practice, only recall is used (Cockburn, Gutwin & Greenberg, 2007) Predicted Results Actual Results Heavily-Used Interfaces 15
  16. 16. Effects of Remembering Instead of Looking Density not an issue for practiced users - not scanning! Heavily-Used Interfaces 16
  17. 17. Effects of Practice – A Deeper Look – Remember instead of look – See and remember patterns instead of features – Automate responses Heavily-Used Interfaces 17
  18. 18. Practiced Users Look at Things Differently Expert Chess Player Novice Chess Player de Groot & Gobet (1996) Heavily-Used Interfaces 18
  19. 19. Experts see Patterns not Objects  Reproduced board while looking and from memory.  Expert chess players did better than novice players.  Experts saw large chunks corresponding to strategiesChase & Simon (1973) Heavily-Used Interfaces 19
  20. 20. Patterns Increase Memory Span Digit span test (Jacobs, 1886) Chase and Ericsson (1973) showed they could increase digit span from 8 to 80. Had person encode the digits into a meaningful structure like running time (235479 = 2 hr, 35 min, 47.9 sec) Kept more in mind because was remembering meaningful patterns instead of individual digits Heavily-Used Interfaces 20
  21. 21. Effects of Seeing Patterns Practiced user sees ordering patterns – not details Can keep more in mind because larger chunks used Heavily-Used Interfaces 21
  22. 22. Effects of Practice – A Deeper Look – Remember instead of look – See and remember patterns instead of features – Automate responses Heavily-Used Interfaces 22
  23. 23. Experts Respond Automatically Repetition enables experts to automate common sequences (Shiffrin & Schneider, 1977). Automated responses are fast Automated responses are harder to change Expert bridge players do worse than novices when rules of whose turn it is change (Frensch & Sternberg, 1989). Heavily-Used Interfaces 23
  24. 24. It is Hard to Change Automated Actions Stroop (1935) demonstrated this – I will present a collection of words – Say the color of the word (not the word!) – Ready? Heavily-Used Interfaces 24
  25. 25. Effects of Automaticity Perform common tasks quickly and automatically Less adaptive to change than deliberate actions Heavily-Used Interfaces 25
  26. 26. How Repeated Use Affects Usability Heavily-Used Interfaces 26
  27. 27. Usability What is a usable interface? “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use.” ISO 9241-11 Heavily-Used Interfaces 27
  28. 28. Usability (cont) Effectiveness – Can users complete tasks? – Focus on errors Efficiency – Can users do the task the way they want to? – Focus on speed Satisfaction – How does user feel after using interface? • Frustrated – Disruptive interface • Neutral – Invisible interface • Proud – Empowering interface How does repeated use affect these dimensions? Heavily-Used Interfaces 28
  29. 29. Repeated Use: Shift from Errors to Speed Heavily-Used Interfaces 29
  30. 30. Designing for Efficiency Heavily-Used Interfaces 30
  31. 31. Make Information Needed for Decisions VisibleDashboards – Make clicking for more detail unnecessaryTables – Show everything needed to know which to select or act upon Heavily-Used Interfaces 31
  32. 32. Make Information Needed for Decisions Visible (cont) Forms – Add needed info to avoid searches Heavily-Used Interfaces 32
  33. 33. Use Dense Screens – If Location is Constant If location of data is constant, a single dense screen is faster than multiple sparse screens (Staggers, 1993) If location of data is variable, dense screens are slower than sparse screens (Rau, Gao, & Liu, 2007) Heavily-Used Interfaces 33
  34. 34. Keep Controls Close to Content Being Acted Upon Fitts’s Law (1954) – Time to move mouse increases when target is • Far away • Small (precise movements take longer) Embed controls in forms Use links as controls Provide contextual (right-click) menus Provide keyboard shortcuts Keep targets large Heavily-Used Interfaces 34
  35. 35. Rely on Knowledge in the Head Distinction popularized by Norman (1988) Direct entry in text boxes rather than menus – Structured fields instead of calendar for past dates (Tullis & Siegel, 2010) Keyboard shortcuts Icons without labels Heavily-Used Interfaces 35
  36. 36. Design For Scanning Keep the layout constant to promote memorization Highlight important information (Neisser, 1967) – 83% improvement in scan time if target made unique – Color, shade, space, slant, size equally effective (Nygren, 1996) Heavily-Used Interfaces 36
  37. 37. Design For Scanning (cont) Use headers. Users scan headers before looking at items (Hornof & Halverson (2003). Heavily-Used Interfaces 37
  38. 38. Design For Scanning (cont) Use tightly-spaced vertical lists instead of horizontal lists. – Tight vertical lists are the fastest (Nygren, 1996) – Each eye fixation picks up multiple items (Hornof & Halverson, 2003). Heavily-Used Interfaces 38
  39. 39. Avoid Modality Switches When possible, enable common sequences to be performed entirely by mouse or by keyboard. It takes 1.7 seconds to: – Move hand from the keyboard to the mouse (0.4 secs) – Move the mouse to a button (1.1. secs) – Click button (0.2 secs) It takes 0.2 seconds to: – Press another key if already on keyboard (Card, Moran & Newell, 1983) Heavily-Used Interfaces 39
  40. 40. Allow FlexibilityInstead of Wizards Use Forms Heavily-Used Interfaces 40
  41. 41. Allow Flexibility (cont) Minimize required fields Data is often incomplete Incomplete data is better than no data – as long as it is retrievable Advanced users come up with unanticipated scenarios Heavily-Used Interfaces 41
  42. 42. Allow Flexibility (cont) Use modal pop-ups for confirmation messages and field values – not general forms. Heavily-Used Interfaces 42
  43. 43. Designing for Skill Development Heavily-Used Interfaces 43
  44. 44. Consider Two Aspects of Skill Development Automaticity Competence Heavily-Used Interfaces 44
  45. 45. Automaticity Depends Upon Constancy Conditions for Automaticity – Constancy – Repetition With repetition, constant features are learned – Layout of screens, widget location & function – E.g., Scanning replaced by recall With more repetition, responses become automated – Motor responses first • Mouse movements • Keyboard sequences – Decisions also become automated Heavily-Used Interfaces 45
  46. 46. Three Phases of Decision Automaticity Knowledge-based decisions – Cues signify novel problem – Consider possible causes and responses Rule-based decisions – Cues signify familiar problem and solution rule – if red rash covers body  measles if sore swollen cheeks  mumps Automated responses – Cues trigger response – Hear RMP go up, shift – Access frequently-used form, fill in fields automatically (Rasmussen, 1983) Heavily-Used Interfaces
  47. 47. Automaticity Tradeoffs Pros – Fast – Enables multi-tasking (Ruthruff et al., 2006) Cons – Inflexible – Insensitive to exceptions Heavily-Used Interfaces 47
  48. 48. Managing Automaticity Replace automaticity with automation – Systems are better at automaticity than humans – 411 Operators “What city and state, please” – Logging events Prevent automaticity when decisions are critical – E.g., require unique reasons or evidence for decisions Disrupt automaticity to increase difficulty – Gaming • Allow a response to be automated • In the next level, make the automated response a wrong response Heavily-Used Interfaces 48
  49. 49. Developing Competence Competence depends upon success-based learning – Skill acquired by success – not failures – Ideal – be successful in tasks that are progressively harder, but still within the range of ability The problem of the match (Hunt, 1961) – Match task difficulty to user competence – Too easy – boredom and no learning – Too hard – discouragement and no learning The gaming industry has nailed this principle Heavily-Used Interfaces 49
  50. 50. Cut the Rope Heavily-Used Interfaces 50
  51. 51. 1. Ropes Heavily-Used Interfaces 51
  52. 52. 1. Ropes (cont) Heavily-Used Interfaces 52
  53. 53. 1. Ropes (cont) Heavily-Used Interfaces 53
  54. 54. 1. Ropes (cont) Heavily-Used Interfaces 54
  55. 55. 2. Bubbles Heavily-Used Interfaces 55
  56. 56. 2. Bubbles (cont) Heavily-Used Interfaces 56
  57. 57. 2. Bubbles (cont) Heavily-Used Interfaces 57
  58. 58. 3. Automatic Ropes Heavily-Used Interfaces 58
  59. 59. 4. Spikes Heavily-Used Interfaces 59
  60. 60. 4. Spikes (cont) Heavily-Used Interfaces 60
  61. 61. 4. Spikes (cont) Heavily-Used Interfaces 61
  62. 62. 5. Air Cushions Heavily-Used Interfaces 62
  63. 63. 6. Spiders Heavily-Used Interfaces 63
  64. 64. 6. Spiders (cont) Heavily-Used Interfaces 64
  65. 65. 6. Spiders (cont) Heavily-Used Interfaces 65
  66. 66. Success-Based Learning Controlling difficulty to match competence builds skill Heavily-Used Interfaces 66
  67. 67. Matching Difficulty to Competence Controlling difficulty to match competence builds skill Possible in non-gaming contexts – Escalation • User can hand-off tasks that are too difficult – Login-Roles • Beginning users have fewer or simpler tasks • Tasks are distributed according to specialty Aligning task difficulty and competence affects – Motivation – Satisfaction Heavily-Used Interfaces 67
  68. 68. Motivation Why does user keep using interface? – Intrinsically motivating - Enjoyable – Extrinsically motivated – Required Intrinsic motivation is fostered by challenges for which the user has competence (and autonomy) to succeed (Deci & Ryan, 1985) Heavily-Used Interfaces 68
  69. 69. Satisfaction Heavily-Used Interfaces 69
  70. 70. Summary Heavily-Used Interfaces 70
  71. 71. Summary Heavily-used interfaces need to be efficient • Make information needed for decisions visible • Use dense screens • Keep controls close to content being acted upon • Rely on knowledge in the head • Design for scanning • Avoid modality switches • Allow flexibility Heavily-used interfaces need to be enjoyable – Match difficulty to competence Efficient and satisfying interfaces will be heavily used Heavily-Used Interfaces 71
  72. 72. ReferencesCard, S., Moran, T. P., & Newell, A. (1983). The psychology of human-computer interaction. Hillsdale, NJ: Erlbaum.Chase, W. G., & Ericsson, K. A. (1982). Skill and working memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 16, pp. 1-58). New York: Academic Press.Chase, W. G., & Simon, H. . (1973). Perception in chess. Cognitive Psychology, 4, 55-81.Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 1, pp. 7-75). Hillsdale, NJ: Erlbaum.Chiesi, H., Spilich, G. J., & Voss, J. F. (1979). Acquisition of domain-related information in relation to high and low domain knowledge. Journal of Verbal Learning and Verbal Behavior, 18, 257-283.Cockburn, A., Gutwin, C., & Greenberg, S. (2007). A predictive model of menu performance. Paper presented at the CHI 2007, San Jose, CA.de Groot, A., & Gobet, F. (1996). Perception and memory in chess: Heuristics of the professional eye. Assen: Van Gorcum.Deci, E., L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press.Egan, D. E., & Schwartz, B. (1979). Chunking in recall of symbolic drawings. Memory and Cognition, 7, 149-158.Ebbinghaus, H. (1913). Memory: A contribution to experimental psychology (H. A. Ruger, and C. E. Bussenius, Trans.). New York: Columbia University, Teachers College. (Original work published 1885).Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47, 381-391. Heavily-Used Interfaces 72
  73. 73. References (cont)Frensch, P. A., & Sternberg, R. J. (1989). Expertise and intelligent thinking: When is it worse to know better? In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 5, pp. 157-188). Hillsdale, NJ: Erlbaum.Hick, W. E. (1952). On the rate of gain in information. Quarterly Journal of Experimental Psychology, 4, 11-26.Hornof, A. J., & Halverson, T. (2003). Modeling user behavior: Cognitive strategies and eye movements for searching hierarchical computer displays Proceedings of the SIGCHI Conference on Human Factors in Computing Systems ChI 03, 249-256.Hunt, J. M. (1961). Intelligence and experience. New York: Ronald.Hyman, R. (1953). Stimulus information as a determinant of reaction time. Journal of Experimental Psychology, 45, 423-432.ISO. (1997). ISO 9241-11: Ergonomic requirements for office work with visual display terminals (VDTs). Part II - Guidelines for specifying and measuring usability. Geneva: International Standards Organisation.Jacobs, J. (1886). IV. -- The need of a society for experimental psychology. Mind, 11, 49-54.Larkin, J., McDermott, J., Simon, D. P., & Simon, H. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335-1342.Neisser, U. (1967). Cognitive Psychology. New York: Appleton-Century-Crofts.Norman, D. A. (1988). The psychology of everyday things. New York: Basic Books.Nygren, E., & Allard, A. (1996, October). Between the clicks: Skilled users scanning of pages. Paper presented at the Second Conference on Human Factors and the Web, Redmond, WA. Heavily-Used Interfaces 73
  74. 74. References (cont)Rasmussen, J. (1983). Skills, rules, knowledge: Signals, signs, and symbols and other distinctions in human performance models. IEEE Transactions on Systems, Man, & Cybernetics, 13, 257-267.Rau, P., Gao, Q., & Liu, J. (2007). The effect of rich Web portal design and floating animations on visual search. International Journal of Human-Computer Interaction, 22(3), 195-216. doi: doi:10.1080/10447310701373022Ruthruff, E., van Selst, M., Johnston, J. C., & Remington, R. (2006). How does practice reduce dual- task interference: Integration, automatization, or just stage-shortening? Psychological Research, 70, 125-142.Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84(2), 127- 190.Staggers, N. (1993). Impact of screen density on clinical nurses computer task performance and subjective screen satisfaction. International Journal of Man-Machine Studies, 39(5), 775-792.Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643-662.Tullis, T. S., & Siegel, M. (2010). Comparison of date entry methods: An update for the Internet age. Paper presented at the Human Factors and Ergonomics Society 54th Annual Meeting, San Francisco. Heavily-Used Interfaces 74
  75. 75. Thank You User Centric, Inc. www.usercentric.comPresenter: Oakbrook Terrace  Chicago  AtlantaRick Omanson 630-320-3900Director info@usercentric.comUser Centric, Inc. Connect with us:www.linkedin.com/in/omanson @UserCentricInc Heavily-Used Interfaces 75