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Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer School on Computational Interaction 2015

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Tutorial on Model-Based User Interface Optimization. Part V: DISCUSSION. Presented by Antti Oulasvirta (Aalto University) at SICSA Summer School on Computational Interaction 2015. Note: This one-day lecture is divided into multiple parts.

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Model-Based User Interface Optimization: Part V: DISCUSSION - At SICSA Summer School on Computational Interaction 2015

  1. 1. Optimization for User Interface Design Antti Oulasvirta, Anna Feit Aalto University SICSA SUMMER SCHOOL ON COMPUTATIONAL INTERACTION GLASGOW
 JUNE 23, 2015 DISCUSSION
  2. 2. CHALLENGES TO UI OPTIMIZATION 1.Formal descriptions of design problems: Formally define design decisions, identify analogues in CS, construct algorithms 2.Modeling: Increased ability to capture mathematical models of human-computer interaction 3.Interactive optimization: Algorithms for interactive optimization that allow iterative design with uncertainty 4.Design paradigms suitable for designer—optimization interaction under uncertainty
  3. 3. The limits of models are the limits of UI optimization
  4. 4. Where are we w.r.t. models? Attention Perception Motor control Biomechanics Spatial behavior Experience Multitasking Choice and decision-making ... Cultural psychology Social cognition Individual differences
  5. 5. Current statistics packages offer poor support for modeling Equation Evaluation [Oulasvirta Proc CHI’14]
  6. 6. Automatic support? Best models Symbolic programming Dataset Generate ! Test Constraints [Oulasvirta Proc CHI’14]
  7. 7. Comparison with 11 existing models in literature Mouse pointing Two-thumb tapping ... Menu selection D,W ID, Telapsed B,I,D,W,Fr Improvements to fitness found in 7 out of 11 cases. 
 Comparable model fitness in others. More predictors, observations, model terms [Oulasvirta Proc CHI’14]
  8. 8. Baseline This paper # Dataset Predictors⇤ n k Model provided in paper R2 ⇤⇤ Best model found⇤⇤⇤ R2 1 Stylus tapping (1 oz)[8] A,W 16 2 a + b log2(2A/W) .966 a + b log2(A/W) .966 2 Reanalyzed data [8] A,We a + b log2(A/We + 1) .987 a + b(log2(log2 A) We) .981 3 Mouse pointing [8] A,W 16 2 a + b log2(A/W + 1) .984 a + b log2(A/W) .973 4 A,We a + b log2(A/We + 1) .980 a + b log10(A/We) .978 5 Trackball dragging [8] A,W 16 2 a + b log2(A/W + 1) .965 a + b log2(A (W3)4) .981 6 A,We a + b log2(A/We + 1) .817 a + b(A/(1 elog10 We )) .941 7 Magic lens pointing [13] A,W, S 16 3 a + b log2(D/S + 1) + c log2(S/2/A) .88 a + b(1 1/A) + cW9 .947 8 Tactile guidance [7] N,I,D 16 3 Eq. 8-9, nonlinear .91, .95 Nonlinear (k = 3) .980 9 Pointing, angular [3] Exp. 2 W, H, ↵, A 310 4 Eq. 33, IDpr, nonlinear .953 Nonlinear (k = 4) .962 10 Two thumb tapping[11] ID,Telapsed 20 6 Eq. 5-6, quadratic .79 a + b(T2 elapsed/ID) .929 11 Menu selection[2] B,I,D,W,Fr 10 6 Eq. 1-7, nonlinear .99,.52 Nonlinear (k = 6) .990 Table 1. Benchmarking automatic modeling against previously published models of response time in HCI. Notes: n = Number of observations (data rows); k = Number of free parameters; * All variable names from the original papers, except I is interface type (dummy coded); ** = As reported in the paper; *** = Some equations omitted due to space restrictions to fixed terms. A second is deciding on a meaningful fit- ness score – we currently use R2 , but this can be changed to cross-validation metrics. A third is model diagnostics. For instance, the use of OLS assumes collinearity and homoge- nous error variance [9]. The latter is probably an unrealistic assumption in many HCI datasets. Analytics are needed to examine the consequences. Fourthly, the equations are not 1. Pointing datasets 1–6 provide the least room to improve, since the R2 s are high to begin with. 2. The method is more successful when there are more predic- tors. The improvements obtained for datasets 7–11 range from small (8, 9, and 11) to medium (7) to large (10). Constraining of model exploration See the full table in the paper Baseline This paper in paper R2 ⇤⇤ Best model found⇤⇤⇤ W) .966 a + b log2(A/W) . We + 1) .987 a + b(log2(log2 A) We) . W + 1) .984 a + b log2(A/W) . We + 1) .980 a + b log10(A/We) . W + 1) .965 a + b log2(A (W3)4) . We + 1) .817 a + b(A/(1 elog10 We )) . + 1) + c log2(S/2/A) .88 a + b(1 1/A) + cW9 . ar .91, .95 Nonlinear (k = 3) . onlinear .953 Nonlinear (k = 4) . c .79 a + b(T2 elapsed/ID) . ar .99,.52 Nonlinear (k = 6) . [Oulasvirta Proc CHI’14]
  9. 9. WHERE MODEL ACQUISITION IS HEADING • Crowdsourcing studies with large samples • Machine learning models (data-driven) • A/B experiments • Adaptive design of experiments • Semi-automatic model building • Interactive model visualization and editing
  10. 10. PHILOSOPHICAL QUESTIONS
  11. 11. IS “PREDETERMINATION” A PLAUSIBLE ASSUMPTION?
  12. 12. Is “full knowledge” enough to determine the best design for a human? What aspects of a design are not predetermined?
  13. 13. What are the limits of mathematical models and simulations of human behavior?
  14. 14. The is—ought problem: Does a full description of how things are imply how things should be?
  15. 15. SICSA SUMMER SCHOOL ON COMPUTATIONAL INTERACTION DAY 2: OPTIMIZATION SUMMARY
  16. 16. SUMMARY OF LESSONS LEARNED • Definition of interface design problems as optimization tasks • Assignment problems • Algorithms: • integer programming, an exact method to find the global optimum • a few metaheuristic methods that are generally useful • Advanced topics in optimization: from robust to dynamic optimization
  17. 17. Model-based interface optimization Guaranteed quality Improved usability Designers become supervisors Designs follow from assumptions Objective criteria for assessment ! Invitation to engineering and 
 computational sciences Transforms the disciplineBetter interfaces
  18. 18. userinterfaces.aalto.fi Papers, Code, Data etc.

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