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Behavioral Aspects of Text Editors

Behavioral Aspects of Text Editors

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Behavioral Aspects of Text Editors

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  1. Behavioral Aspects of Text Editors David W. Embley, George Nagy University of Nebraska, Lincoln

  2. Assumptions for readers • Familiar with basic vocabulary of computer science • Sufficient exposure to various text and program editors • Innocent of any formal training in psychology QED, CMS, TECO, Wylbur, WIDJET and UNIX

  3. Interactive Text Editors • Frequently, Primary means of interaction with computer • Manuscript creation • Programming • File System Maintenance • Email • Important to make their use easy • Editors • General Purpose Interactive editors • QED, CMS, TECO, Wylbur, WIDJET and UNIX • Language dependent editors • BASIC, APL, LISP

  4. Editor Design and Evaluation • Everyone has an opinion, but no consensus • Some established means: • Introspection • Field Studies/Observations • Formal Analysis • Controlled Experiments • Psychological Models ?

  5. Psychological Models Characterize human performance Goal: To predict human behavior in a restricted environment while performing a set of tasks Example ( of an editing task) Introspection Our own intuition and experience, is what we depend on when we assume that we know as much about the topic as the next person and are too lazy to look further

  6. Applicable areas of Psychology • Cognitive Psychology • Study of higher mental processes • LLUMPRT • Well studied area but limited application to study of text editors

  7. Overview • Section 1 : Temporal Models • Section 2: Impact of Editor structure and command languages • How do different editors differ ? • Section 3: Stimulus and response studies of input devices : Mouse wins

  8. Performance Time Considerations

  9. Objective • To minimize the time incurred by a user performing a number of editing tasks over a period of time • Depends on numerous factors • Expertise of the user • Learning methods and procedures • User alertness and motivation • Out of Paper • … • Some are within our control and can be improved

  10. Predictive Models The Keystroke Model [CARD] Total time = sum of time required to perform individual unit tasks To acquire a mental representation of the task Perform and execute it

  11. ?

  12. Example • Replacing one word of arbitrary length with a five letter word

  13. Model Verification • 12 subjects, 4 different editing tasks, 3 different editors • Tasks: • Simple word substitution • Moving a sentence

  14. Results • Observed times and predicted times match mostly • Exploring More or less detailed models [CARD]

  15. The Embley Model • A simpler model for line-oriented editors • Objective • Comparing program editor performance as a function of time required by a user to perform editing tasks

  16. The Model Acquisition time and mental time combined m = number of command response pairs Tc = delay per command = mental prep. Time + computer response time n = number of keystrokes Tk = time per keystroke

  17. GOMS Model • Attempts to explain How an expert user accomplishes routine editing tasks, not just time constituents

  18. Can adjust to desired level of detail • Example Substitute OR Specify substitute command – specify argument number 1 - specify argument number 2 – enter command Which one is more accurate ? ?

  19. Experimental Studies • Several variations were explored • 10 different GOMS models • 16 second operator duration, 8, 4, 2, 0.5 ( “type an ‘s’, home hands on keyboard) • 5 participated, only 1 studied DATA Derivation Data Cross-validationData Prediction rules for operator sequences Estimates for operator duration For calculation of unit task time using derivation data results

  20. Predicting the task accomplishment method • Objective: To determine whether a set of simple selection rules could account for the methods user select. • The Experiment • 3 subjects • Teletypewriter and CRT

  21. Findings • Each subject appeared to have a dominant method – the first rule • S2 applied different dominant method for different devices – speed difference • Selection of methods depends on feature of task – e.g. : • Locating a line : number of lines between current line and target line Users are able to quickly select near-optimal methods by having assimilated heuristic rules based on a few pertinent task features

  22. Contribution of Errors • Error ignored in previous experiments • Even for experts: 5-30% time in errors and error corrections For accurate Prediction, errors must also be considered

  23. Roberts’s Experiments • 4 experts  4 separate tasks • Human observer noted time consumed by significant errors (> 30 seconds ) • Findings : Much of the subject-to-subject variation is due to error rates • For error free data, variation can be attributed to editors than to the subjects

  24. Applying the Keystroke model • Errors were ignored • Optimal Method prediction  Predictions 25-30% too low • Actual key sequence records  only 87% accounted for. • Remaining time  Unknown mental activities

  25. Advantage of Keystroke Model • Assumes that user is so practiced that: • Method selection time would be nil • Choices optimal • Entry Flawless • Provides an upper bound for the editing time • Comparison between predicted time and observed time  relatively large difference indicates that editor is difficult to use optimally

  26. Effects of Computer Response time

  27. Effects of Computer Response time • System Delay and Unpredictability  Affects user Productivity and Satisfaction • Editing : Any perceptible delay may prove irritating • R.B. Miller  Immediate response is not a universal requirement in interactive computing • Lists various class of user actions and allowable delays  “best guesses”

  28. Miller’s experiment • Effects of varying CRT display rates and output delays on user performance • Delay: Increasing the display rate from 1200 to 2400 baud produced no significant performance or attitude changes • Variance: Increasing the variability of the output display rate produced a significant deterioration in both performance and attitude

  29. Grossberg’s Experiment • Problem Solving Context: System response time has little effect on performanceWhy? • Users simply adjust their tactics to make best use of their time on system • Response Times in problem solving activities varied : 1,4,16 and 64 • As mean delay increased , users became more cautious and deliberate • However, no definite effect on time required to reach solution ?

  30. Transferring to the Editing environment • Editing  a routine cognitive skill • Additional mental preparation time not useful, in fact would interfere with the task completion time ( because of irritation ) • Experiments are always motivated to complete their tasks, but not in the real world

  31. Editor Design Considerations

  32. How can we make editor easy to use ? • Depends on the • Command language of the editor • Underlying structure ( editor states or modes…) • Tradeoff: • Our inability to learn, remember, and effectively use large complex command sets vsdesire to achieve editing objectives within minimum time • Limits range of design options

  33. Many approaches • Popular wisdom • Principle of Predictable Behavior • User Engineering Principles • Observation • Dzida’s Questionnaire study: User perceived quality as a multi-dimensional concept • Identified 7 major categories • Learning Process • 1. Self teaching through trial/error with machine feedback most effective • 2. Anxiety decreased learning ?

  34. Controlled Experiments • Command language structure and learning ability • Whether user options are good for everyone’s performance ? • Experiment: Two versions of editors • Inflexible : full commands, no abbrv., extra spaces, or defaults allowed • Flexible : lot of freedom ?

  35. Results • Flexibility pros and cons • More prone to syntax errors • Completed tasks faster • Role of English-similar commands • It is more helpful ? ?

  36. Tested with two versions of same editor (NOS) • Typical Notational Syntax • Legitimate English phrases • 24 paid subjects • English version • Completed more tasks • Error rate was lower • Editing efficiency was better  Surface syntax of an editor is surprisingly important from human engineering point of view

  37. Input and Output Devices Psychological and Human factors underlying design and use of keyboards, screen displays and pointing devices

  38. Key Entry • The most common means of encoding letters and numbers Keyboard Devices Oldest typewriters Teletypewriters Electric typewriters ? Detailed research exists in keyboard design

  39. Detailed research exists in keyboard design • Keyboard layout (e.g. QWERTY) • Numeric keys • Standard Key size • Slope of keyboard • Key depression force required • Key displacement • Type of Kinesthetic feedback from key actuation

  40. Typing speed • Some factors • Finger ballistics • Reaction time • Motor learning • Short term memory • Human information processing capacity • Average single finger tapping rate: 6 keys/s Little finger  Index Finger = 20% increase

  41. Some interesting stats • Good typists: 0.2 secs per keystroke ( 50 words/min) • Less Frequent users: 0.7 secs • Experienced Typists: 0.08 secs (12 taps/sec) • Typing with alternate hands: 25% faster than with one hand •  Control the necessary echo output rate for a display

  42. Effects of Training • Poor typing habits are difficult to shed • Self-taught typists do not reach even half the speed expected from entry level typists • Worth considering the benefits of specialized training

  43. The Shaffer and Hardwick Experiment • Limitation of human information processing capabilities • Material: • Difficult but coherent text • Randomly selected words • Short words of Random character sequences • Explanation • “Acquisition of a hierarchy of habits” ( ability to type a whole word as a single unit) • Able to read farther ahead, as opposed to random characters ?

  44. 0.159 secs per keystroke main() { unsigned paddr,pdata; LOOP: printf("Input port address (hex): "); scanf("%x",&paddr); pdata = inp(paddr); printf("Port(%xh) = %xh\n",paddr,pdata); goto LOOP; return 0; } More than double the time 0.162 secs per keystroke