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Optimizing the Keyboard

Optimizing the Keyboard. PSY/ORF 322 Final Project Jessica Blankshain, James Ma, and Robert J. Moore. Purpose. Develop a keyboard optimization algorithm. Utilize Fitts’s Law and other aspects of the Card-Moran-Newell (CMN) model of the human processor.

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Optimizing the Keyboard

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  1. Optimizing the Keyboard PSY/ORF 322 Final Project Jessica Blankshain, James Ma, and Robert J. Moore

  2. Purpose • Develop a keyboard optimization algorithm. • Utilize Fitts’s Law and other aspects of the Card-Moran-Newell (CMN) model of the human processor. • Demonstrate that neither the QWERTY nor the Dvorak keyboard layout is an optimal configuration of the keys on the three-row keyboard. • These layouts can be improved upon using quantitative methods.

  3. History: QWERTY • Old typewriters used typebars and had an “invisible” printing point, leading to frequent jams. • There is speculation that they intentionally placed all the letters of the word “typewriter” in the top row for quick and easy demonstration.

  4. History: Dvorak • In the 1930’s, August Dvorak and William Dealey developed and patented a new layout, known as the Dvorak Simplified Keyboard (DSK). • Designed to improve typing efficiency and user comfort. • Reasoning for layout was strictly qualitative.

  5. Methods: Scale Issues Why Not Examine Every Possible Permutation? At one keyboard per second, that’s 8.41*1024 years. The sun will burn out after 5*109 of them.

  6. Methods: Keyboard Population • Rather than look at entire keyboards, examine the influence of specific keys in specific locations. • There are only 30*30 = 900 key-position pairs. • We can randomly populate a large number of keyboards, then see how certain keys in certain positions influence their performance.

  7. Methods: Scoring Keyboards • First, need a figure of merit for keyboard performance: • Compile a large text file and calculate how long it would take an average human to type that entire file using that keyboard. • These figures are derived from the CMN model and Fitts’s Law.

  8. Methods: Applying CMN • Used Fitts’s Law to determine how long it would take a typist to move a finger from one key to another. • Determined inter-row and inter-finger transition times. • From this data, could determine the time to transition from any finger in any position to any other. • See paper for quantitative details.

  9. Methods: Compiling a Text Sample • ~500KB of raw text • 80,720 Characters • Sources: • Popular Novels • Famous Speeches • TV Transcripts • Rap Lyrics

  10. Methods: Algorithmic Flow • Simulate 90,000 keyboards. • Expect to see each key-position combination 3,000 times. • Enough data to determine the best possible key-position combination. • “Lock-In” that key. • Repeat, simulating only the remaining 29 keys. • Continue until entire keyboard populated.

  11. Results • Outperformed QWERTY by 33.3%. • Outperformed Dvorak by 6.2%.

  12. Conclusions • Our method was successful. • While DSK is a quantifiable improvement over QWERTY, our model is more efficient than both. • Extensions: Software’s robustness allows for keyboards customized to users or industries.

  13. Naming MNT RSH = Mt. Rushmore Mount RushBoard

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