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User Modeling of Assistive Technology

User Modeling of Assistive Technology. Rich Simpson. 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. The Problem….

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User Modeling of Assistive Technology

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  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 (ts) • Decide what key to press (d) • Press Key (tk) • Repeat…

  26. Koester’s Model of Word Prediction • S=number of searches/number of characters • K=number of keystrokes/number of characters • Twp=(S)(ts) + (K)(tk+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, ts, K and tk? • Number of searches (S) • When does the list appear? (Show) • When does the list disappear? (Hide) • List search time (ts) • 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. Since you can’t set S and K, what good are these models? • You can measure ts and tk • 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

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