Predictive Evaluation Predicting performance

Predictive Models Translate empirical evidence into theories and models that can influence design. Performance measures – Quantitative – Time prediction – Working memory constraints Competence measures Focus on certain details, others obscured

Two Types of User Modeling Stimulus-Response – Practice law – Fitt’s law Cognitive – human as interpreter/predictor – based on Model Human Processor (MHP) – Key-stroke Level Model Low-level, simple – GOMS (and similar) Models Higher-level (Goals, Operations, Methods, Selections)

Power law of practice T n = T 1 n -a – T n to complete the nth trial is T 1 on the first trial times n to the power -a; a is about.4, between.2 and.6 – Skilled behavior - Stimulus-Response and routine cognitive actions Typing speed improvement Learning to use mouse Pushing buttons in response to stimuli NOT learning

Power Law: T n = T 1 n -a If first trial (T 1 ) takes 5 seconds, how long will future trials take? When will improvements level off? (a = -0.4)

Uses for Power Law of Practice Use measured time T 1 on trial 1 to predict whether time with practice will meet usability criteria, after a reasonable number of trials – How many trials are reasonable? Predict how many practices will be needed for user to meet usability criteria – Determine if usability criteria is realistic

Fitts’ Law Models movement times for selection tasks Paul Fitts: war-time human factors pioneer Basic idea: Movement time for a well- rehearsed selection task – Increases as the distance to the target increases – Decreases as the size of the target increases

Moving Move from START to STOP D W START STOP Index of Difficulty: ID = log 2 ( 2D/W ) (in unitless bits) width of target distance

Movement Time MT = a + b*ID or MT = a + b log 2 (2D/W) MT ID Empirical measurement establishes constants a and b Different for different devices and different ways the same device is used.

Questions What do you do in 2D? – h x l rect: one way is ID = log 2 (d/min(w, l) + 1) – Should take into account direction of approach

Applications When does it apply? How used in interface design?

Keystroke Level Model (KLM) Also developed by Card, Moran, and Newell (1983) Skilled users performing routine tasks – Assumes error-free performance Analyze only observable behaviors – Keystrokes, mouse movements Assigns times to basic human operations - experimentally verified

KSLM Accounts for Keystroking T K Mouse button pressT B Pointing (typically with mouse) T P Hand movement between keyboard and mouse T H Drawing straight line segments T D “Mental preparation” T M System Response time T R

Step One : MS Word Find Command Use Find Command to locate a six character word – H (Home on mouse) – P (Edit) – B (click on mouse button - press/release) – P (Find) – B (click on mouse button) – H (Home on keyboard) – 6K (Type six characters into Find dialogue box) – K (Return key on dialogue box starts the find)

Using KSLM - Step Two Place M operators Rule 0a. In front of all K’s that are NOT part of argument strings (ie, not part of text or numbers) Rule 0b. In front of all P’s that select commands (not arguments)

Step Two : MS Word Find Command H (Home on mouse) MP (Edit) B (click on mouse button) MP (Find) B (click on mouse button) H (Home on keyboard) 6K (Type six characters) MK (Return key on dialogue box starts the find) Rule 0b: P selects command Rule 0a: K is argument

Using KSLM - Step 3 Remove M’s according to heuristic rules (Rules relate to chunking of actions) Rule 1. Anticipated by prior operation – H MP ->HP (pointing to menu item is anticipated by moving hand to mouse) Rule 2. If string of MKs is a single cognitive unit (such as a command name), delete all but first – MKMKMK -> MKKK (same as M3K) (type “run is a chunk) Rule 3. Redundant terminator, such as )) or rtn rtn Rule 4. If K terminates a constant string, such as command-rtn, then delete M M2K(ls)MK(rtn) -> M2K(ls)K(rtn) (typing “ls” command in Unix followed by rtn is a chunk)

H (Home on mouse) MP (Edit) B (click on mouse button) MP (Find) B (click on mouse button) H (Home on keyboard) 6K (Type six characters) MK (Return key on dialogue box starts the find) Step 3 : MS Word Find Command Rule 4 Keep M Rule 1 delete M H anticipates P

Using KSLM - Step 4 Plug in real numbers from experiments – K:.08 sec for best typists,.28 average, 1.2 if unfamiliar with keyboard – B: down or up secs; click secs – P: 1.1 secs – H: 0.4 secs – M: 1.35 secs – R: depends on system; often less than.05 secs

Step 4 : MS Word Find Command H (Home on mouse) P (Edit) B (click on mouse button - press/release) MP (Find) B (click on mouse button) H (Home on keyboard) 6K (Type six characters into Find dialogue box) MK (Return key on dialogue box starts the find) Timings – H = 0.40, P = 1.10, B = 0.20, M = 1.35, K = 0.28 – 2H, 2P, 2B, 2M, 7K Predicted time = 8.06 secs

Example: MS Windows Menu Selection Get hands on mouse Select from menu bar with click of mouse button The “pull down” menu appears Select desired item from the pull down menu

Step 1: MS Windows Menu H (Home on mouse) P (point to menu bar item) B (left-click with mouse button) P (point to menu item) B (left-click with mouse button)

Step 2: MS Windows Menu - Add M’s H (get hand on mouse) MP (point to menu bar item) B (left-click with mouse button) MP (point to menu item) B (left-click with mouse button) Rule 0b: P selects command

Step 3: MS Windows Menu - Delete M’s H (get hand on mouse) MP (point to menu bar item) B (left-click with mouse button) MP (point to menu item) B (left-click with mouse button) Keep M Rule 1 M anticipated by H

Step 4: MS Windows Menu Calculate Time H (get hand on mouse) P (point to menu bar item) B (left-click with mouse button) MP (point to menu item) B (left-click with mouse button) Textbook timings (all in seconds) – H = 0.40, P = 1.10, B = 0.20, M = 1.35 – H, 2P, 2B, 1 M Total predicted time = 4.35 sec

Alternative Menu Selection Operator sequence – H(mouse)P(to menu item)B(down)PB(up) Now place Ms – H(mouse)MP(to menu item)B(down)MPB(up) Selectively remove Ms – H(mouse)MP(to menu item)B(down)MPB(up) Textbook timings (all in seconds) H = 0.40, P = 1.10, B = 0.10 for up or down, M = 1.35 H, 2P, 2 B, 1 M Total predicted time = 4.15 sec Alternative is predicted to be.2 secs faster than typical, about 5% Rule 0b Rule 1 Delete H anticipates P

KSLM Comparison Problem Are keyboard accelerators always faster than menu selection? Use MS Windows to compare – Menu selection of File/Print (previous example estimated 4.35 secs.) – Keyboard accelerator ALT-F to open the File pull down menu P key to select the Print menu item Assume hands start on keyboard

KSLM Comparison: Keyboard Accelerator for Print Use Keyboard for ALT-F P (hands already there) – K(ALT)K(F)K(P) – MK(ALT)MK(F)MK(P) – MK(ALT)K(F)MK(P) 2M + 3K = K Times for K based on typing speed – Good typist, K = 0.12 s, total time = 3.06 s – Average typist, K = 0.28 s, total time = 3.54 s – Non-typist, K = 1.20 s, total time = 6.30 s – Time with mouse was 4.35 sec Conclusion: Accelerator keys not faster than mouse for non- typists First K anticipates second K

What if you started on the mouse? Use Keyboard for ALT-F P (hands already there) – H K(ALT)K(F)K(P) – H MK(ALT) K(F)MK(P) H + 2M + 3K = K Times for K based on typing speed – Good typist, K = 0.12 s, total time = 3.46 s – Average typist, K = 0.28 s, total time = 3.94 s Menu selection was 4.35 sec when starting on keyboard – To start on mouse its 4.35 – H = 3.95 seconds Conclusion: Hmmm… not faster for average typists

Comparison Consider: compare selecting a menu item in a right-click popup menu vs. selecting the same menu item from menu in menu bar What would Fitt’s Law say? What would KSLM say?

One more practice Draw through text and make it bold – By pointing to BOLD icon in floating palette – By selecting BOLD from pull-down menu