1. SBD: Usability Evaluation Chris North CS 3724: HCI

2. Problem scenarios summative evaluation Information scenarios claims about current practice analysis of stakeholders, field studies Usability specifications Activity scenarios Interaction scenarios iterative analysis of usability claims and re-design metaphors, information technology, HCI theory, guidelines formative evaluation DESIGN ANALYZE PROTOTYPE & EVALUATE Scenario-Based Design

3. Evaluation Formative vs. Summative Analytic vs. Empirical

4. Usability Engineering Reqs Analysis Evaluate many iterations Design Develop

5. Usability Engineering Formative evaluation Summative evaluation

6. Usability Evaluation Analytic Methods: Usability inspection, Expert review Heuristic: Nielsen’s 10 Cognitive walk-through GOMS analysis Empirical Methods: Usability Testing –Field or lab –Observation, problem identification Controlled Experiment –Formal controlled scientific experiment –Comparisons, statistical analysis

7. User Interface Metrics Ease of learning Ease of use User satisfaction

8. User Interface Metrics Ease of learning learning time, … Ease of use performance time, error rates… User satisfaction surveys… Not “user friendly”

9. Usability Testing

10. Formative: helps guide design Early in design process when architecture is finalized, then its too late! Small # of users Usability problems, incidents Qualitative feedback from users Quantitative usability specification

11. Usability Specification Table Benchmark task Worst case Planned Target Best case (expert) Observed Find most expensive house for sale? 1 min.10 sec.3 sec.??? sec … e.g. frequent tasks should be fast metrics

12. Usability Test Setup Set of benchmark tasks Derived from scenarios (Reqs analysis phase) Derived from claims analysis (Design phase) Easy to hard, specific to open-ended Coverage of different UI features E.g. “Find the 5 most expensive houses for sale” Different types: learnability vs. performance Consent forms Not needed unless recording user’s face/voice (new rule) Experimenters: Facilitator: instructs user Observers: take notes, collect data, video tape screen Executor: run the prototype for faked parts Users Solicit from target user community (Reqs analysis) 3-5 users, quality not quantity

13. Usability Test Procedure Goal: mimic real life Do not cheat by helping them complete tasks Initial instructions “We are evaluating the system, not you.” Repeat: Give user next benchmark task Ask user to “think aloud” Observe, note mistakes and problems Avoid interfering, hint only if completely stuck Interview Verbal feedback Questionnaire ~1 hour / user

14. Usability Lab E.g. McBryde 102

15. Data Note taking E.g. “&%$#@ user keeps clicking on the wrong button…” Verbal protocol: think aloud E.g. user thinks that button does something else… Rough quantitative measures HCI metrics: e.g. task completion time, … Interview feedback and surveys Video-tape screen & mouse Eye tracking, biometrics?

16. Analyze Initial reaction: “stupid user!”, “that’s developer X’s fault!”, “this sucks” Mature reaction: “how can we redesign UI to solve that usability problem?” the data is always right Identify usability problems Learning issues: e.g. can’t figure out or didn’t notice feature Performance issues: e.g. arduous, tiring to solve tasks Subjective issues: e.g. annoying, ugly Problem severity: critical vs. minor

17. Cost-Importance Analysis Importance 1-5: (task effect, frequency) 5 = critical, major impact on user, frequent occurance 3 = user can complete task, but with difficulty 1 = minor problem, small speed bump, infrequent Ratio = importance / cost Sort by this, highest to lowest 3 categories: Must fix, next version, ignored ProblemImportanceSolutionsCostRatio I/C

18. Refine UI Solve problems in order of: importance/cost Simple solutions vs. major redesigns Iterate: Test, refine, test, refine, test, refine, … Until?

19. Refine UI Solve problems in order of: importance/cost Simple solutions vs. major redesigns Iterate: Test, refine, test, refine, test, refine, … Until? Meets usability specification

20. Examples Learnability problem: Problem: user didn’t know he could zoom in to see more… Potential solutions: –Better labeling: Better zoom button icon, tooltip –Clearer affordance: Add a zoom bar slider (like google maps) –… –NOT: more “help” documentation! You can do better. Performance problem: Problem: user took too long to repeatedly zoom in… Potential solutions: –Faster affordance: Add a real-time zoom bar –Shortcuts: Icons for each zoom level: state, city, street –…

21. Project (step 6): Usability Test Usability Evaluation: >=3 users: Not (tainted) HCI students ~10 benchmark tasks Simple data collection (Biometrics optional!) Exploit this opportunity to improve your design Report: Procedure (users, tasks, specs, data collection) Usability problems identified, specs not met Design modifications

22. Controlled Experiments

23. Usability test vs. Controlled Expm. Usability test: Formative: helps guide design Single UI, early in design process Few users Usability problems, incidents Qualitative feedback from users Controlled experiment: Summative: measure final result Compare multiple UIs Many users, strict protocol Independent & dependent variables Quantitative results, statistical significance Engineering oriented Science oriented

24. What is Science?

25. What is Science? Measurement Modeling Engineering Science Phenomenon

26. Scientific Method? 1. 2. 3. 4.

27. Scientific Method 1.Form Hypothesis 2.Collect data 3.Analyze 4.Accept/reject hypothesis How to “prove” a hypothesis in science?

28. Scientific Method 1.Form Hypothesis 2.Collect data 3.Analyze 4.Accept/reject hypothesis How to “prove” a hypothesis in science? Easier to disprove things, by counterexample Null hypothesis = opposite of hypothesis Disprove null hypothesis Hence, hypothesis is proved

29. Example Typical question: Which visualization is better for which user tasks? Spotfirevs.TableLens

30. Cause and Effect Goal: determine “cause and effect” Cause = visualization tool (Spotfire vs. TableLens) Effect = user performance time on task T Procedure: Vary cause Measure effect Problem: random variation Cause = vis tool OR random variation? Real world Collected data random variation uncertain conclusions

31. Stats to the Rescue Goal: Measured effect unlikely to result by random variation Hypothesis: Cause = visualization tool (e.g. Spotfire ≠ TableLens) Null hypothesis: Visualization tool has no effect (e.g. Spotfire = TableLens) Hence: Cause = random variation Stats: If null hypothesis true, then measured effect occurs with probability > random variation) Hence: Null hypothesis unlikely to be true Hence, hypothesis likely to be true

32. Variables Independent Variables (what you vary), and treatments (the variable values): Visualization tool: »Spotfire, TableLens, Excel Task type: »Find, count, pattern, compare Data size (# of items): »100, 1000, 1000000 Dependent Variables (what you measure) User performance time Errors Subjective satisfaction (survey) HCI metrics

33. Example: 2 x 3 design n users per cell Task1Task2Task3 Spot- fire Table- Lens Ind Var 1: Vis. Tool Ind Var 2: Task Type Measured user performance times (dep var)

34. Groups “Between subjects” variable 1 group of users for each variable treatment Group 1: 20 users, Spotfire Group 2: 20 users, TableLens Total: 40 users, 20 per cell “With-in subjects” (repeated) variable All users perform all treatments Counter-balancing order effect Group 1: 20 users, Spotfire then TableLens Group 2: 20 users, TableLens then Spotfire Total: 40 users, 40 per cell

35. Issues Eliminate or measure extraneous factors Randomized Fairness Identical procedures, … Bias User privacy, data security IRB (internal review board)

36. Procedure For each user: Sign legal forms Pre-Survey: demographics Instructions »Do not reveal true purpose of experiment Training runs Actual runs »Give task »measure performance Post-Survey: subjective measures * n users

37. Data Measured dependent variables Spreadsheet: UserSpotfireTableLens task 1 task 2 task 3 task 1 task 2 task 3 user112 s451041351138 user21638971048116 …

38. Step 1: Visualize it Dig out interesting facts Qualitative conclusions Guide stats Guide future experiments

39. Step 2: Stats Task1Task2Task3 Spot- fire 37.254.5103.7 Table- Lens 29.853.2145.4 Ind Var 1: Vis. Tool Ind Var 2: Task Type Average user performance times (dep var)

40. TableLens better than Spotfire? Problem with Averages? Spotfire TableLens Avg Perf time (secs)

41. TableLens better than Spotfire? Problem with Averages? lossy Compares only 2 numbers What about the 40 data values? (Show me the data!) Spotfire TableLens Avg Perf time (secs)

42. The real picture Need stats that compare all data What if all users were 1 sec faster on TableLens? What if only 1 user was 20 sec faster on TableLens? Spotfire TableLens Avg Perf time (secs)

43. Statistics t-test Compares 1 dep var on 2 treatments of 1 ind var ANOVA: Analysis of Variance Compares 1 dep var on n treatments of m ind vars Result: p = probability that difference between treatments is random (null hypothesis) “statistical significance” level typical cut-off: p < 0.05 Hypothesis confidence = 1 - p

44. In Excel

45. p < 0.05 Woohoo! Found a “statistically significant” difference Averages determine which is ‘better’ Conclusion: Cause = visualization tool (e.g. Spotfire ≠ TableLens) Vis Tool has an effect on user performance for task T … “95% confident that TableLens better than Spotfire …” NOT “TableLens beats Spotfire 95% of time” 5% chance of being wrong! Be careful about generalizing

46. p > 0.05 Hence, no difference? Vis Tool has no effect on user performance for task T…? Spotfire = TableLens ?

47. p > 0.05 Hence, no difference? Vis Tool has no effect on user performance for task T…? Spotfire = TableLens ? NOT! Did not detect a difference, but could still be different Potential real effect did not overcome random variation Provides evidence for Spotfire = TableLens, but not proof Boring, basically found nothing How? Not enough users Need better tasks, data, …

48. Data Mountain Robertson, “Data Mountain” (Microsoft)

49. Data Mountain: Experiment Data Mountain vs. IE favorites 32 subjects Organize 100 pages, then retrieve based on cues Indep. Vars: UI: Data mountain (old, new), IE Cue: Title, Summary, Thumbnail, all 3 Dependent variables: User performance time Error rates: wrong pages, failed to find in 2 min Subjective ratings

50. Data Mountain: Results Spatial Memory! Limited scalability?