1. User Modeling of Assistive Technology Rich Simpson

2. The Problem… The most challenging aspect of designing a computer access system for a client is predicting and accommodating a client’s performance in six months based on two hours of interaction with that client.

3. The Problem… Clients may only see the clinician once, and that visit only lasts for a few hours There may be multiple potential solutions Each potential solution may have multiple configuration options The client has little or no experience with assistive technology upon which to base decisions

4. The Problem… Often, the assistive technology that’s easiest to use at first will be less efficient in the long run  Morse Code vs Row-Column Scanning

5. The Problem… What we want:  We want to know how well each potential solution would work for a client if the client had six months to practice What we have:  Observations in the clinic  Assistive Technology Lending Library

6. Keystroke-Level Modeling “A simple model for the time it takes [an expert] user to perform a task with a given method on an interactive computer system.” Predictive rather than descriptive or explanatory Based on intuition rather than observation Intended to allow comparisons between two or more designs without having to run user trials

7. Keystroke-Level Modeling What does “expert” mean?  Knows how to do the task  Doesn’t make mistakes  Consistent time for each action

8. Keystroke-Level Modeling Operators  K - Keystroking  P - Pointing  H - Homing  D - Drawing  M - Thinking  R - System Responding

9. Keystroke-Level Modeling Keystroking (K)  Typing speed  Can range between 0.08 and 1.20 seconds for able- bodied adults using a standard keyboard

10. Keystroke-Level Modeling Pointing (P)  Based on Fitts’ Law

11. Keystroke-Level Modeling Mental Operations (M)  The time to mentally prepare to execute physical operators  In front of the first K of a string  In front of all Ps that select commands

12. Keystroke-Level Modeling An example: saving a file  Move mouse to File menu  Press mouse button  Move mouse to “Save” option  Press mouse button  Type in the name of the file  Press the enter button

13. Keystroke-Level Modeling An example: saving a file  Decide what to do (M)  Move mouse to File menu (P)  Press mouse button (K)  Decide what to do (M)  Move mouse to “Save” option (P)  Press mouse button (K)  Pick a name for the file (M)  Type in the name of the file (K x length of name)  Decide what to do (M)  Press the enter key (K)

14. Keystroke-Level Modeling Simplifications  Fitts’ Law vs Steering Law  All movements (P, K) take the same amount of time  No actions overlap

15. The Problem… The most challenging aspect of designing a computer access system for a client is predicting and accommodating a client’s performance in six months based on two hours of interaction with that client.

16. What is Word Prediction? Word prediction is used to reduce the number of keystrokes required to generate text. The computer supplies a list of “best guesses” for the word the user is currently entering, and when the word appears it may be selected from the list with a single keystroke.

17. What is Word Prediction? Word prediction is used to reduce the number of keystrokes required to generate text. The computer supplies a list of “best guesses” for the word the user is currently entering, and when the word appears it may be selected from the list with a single keystroke.

18. What is Word Prediction? Word prediction is used to reduce the number of keystrokes required to generate text. The computer supplies a list of “best guesses” for the word the user is currently entering, and when the word appears it may be selected from the list with a single keystroke.

19. What is Word Prediction? Word prediction is used to reduce the number of keystrokes required to generate text. The computer supplies a list of “best guesses” for the word the user is currently entering, and when the word appears it may be selected from the list with a single keystroke.

20. What is Word Prediction? Word prediction is used to reduce the number of keystrokes required to generate text. The computer supplies a list of “best guesses” for the word the user is currently entering, and when the word appears it may be selected from the list with a single keystroke.

21. Why doesn’t Word Prediction always increase text entry rate? Word Prediction doesn’t necessarily increase the speed with which a person can enter text because it trades off physical effort for cognitive effort. The configuration of a word prediction system can have a significant effect on a user’s performance.

22. Configuring Word Prediction Show: Number of keystrokes entered before list appears Hide: The number of keystrokes entered after list appears before it disappears Llen: Maximum number of words in list MWS: Minimum number of letters in each word in list

23. The Questions… Will word prediction increase text entry rate for a client? How should word prediction be configured to maximize text entry rate?

24. Koester’s Model of Word Prediction Search word prediction list Decide what key to press Press Key Repeat…

25. Koester’s Model of Word Prediction Search word prediction list (t s ) Decide what key to press (d) Press Key (t k ) Repeat…

26. Koester’s Model of Word Prediction S=number of searches/number of characters K=number of keystrokes/number of characters T wp =(S)(t s ) + (K)(t k +M) So the question is…

27. how do these… Show: Number of keystrokes entered before list appears Hide: The number of keystrokes entered after list appears before it disappears Llen: Maximum number of words in list MWS: Minimum number of letters in each word in list

28. influence S, t s, K and t k ? Number of searches (S)  When does the list appear? (Show)  When does the list disappear? (Hide) List search time (t s )  Length of list (Llen)  Size of words in list (MWS) Number of keystrokes (K)  When does the list appear? (Show)  When does the list disappear? (Hide)  Length of list (Llen)  Size of words in list (MWS)

29. Since you can’t set S and K, what good are these models?

30. You can measure t s and t k It’s hard to measure M (which Koester calls d) You can simulate user performance over a range of values for Show, Hide, Llen and MWS The most promising configurations can be compared in trials with the client

31. Experimental Validation Six subjects with disabilities ABA design  A was a “default” condition: list always displayed, six words in list, no minimum number of letters  B was chosen using the model and observations during the first A phase For three subjects, B was 61% faster than A For the other three subjects, B was 20% faster