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Individual Differences in Human-Computer Interaction
HMI
Yun Hwan Kang
Contents Introduction How big are Individual Differences in Human-
Computer Interaction? What predicts Differences in Performance? Accommodating User Differences Goals in Designing for User Differences
Introduction Usually differences among users are not
major concern of commercial computer interface designers
But should focus on differences among users
Introduction Because
1. Differences among people > Differences in system design or training procedures
To deal with differences among people can improve the performance
2. Personnel selection testing cannot be applied to many settings where humans interact with computers
Flexbility afforded by computers can broaden the definition of ‘the right person’ for a job.
3. Now the technology & understanding is enough to accommodate more user differences!
How big are Individual Differences in Human-Computer Interaction? To document the magnitude of individual
differences in human computer interaction Selecting Computer-based Tasks to Analyse
Text editing Information search Programming These tasks are commonly performed, can be
done with large samples & diverse type of task, high mental process, perceptual-motor skill & content domain knowledge as well.
How big are Individual Differences in Human-Computer Interaction? Used statistics
Basic measure : The time required by trained users to complete a task
Task completion time Indices
The sample maximum, minimum and their ratio Not stable with small samples
The first & third quartile scores and their ratio Stable with small samples
The standard deviation and the coefficient of variation Stable but hard to comprehend, only number?
How big are Individual Differences in Human-Computer Interaction? Text editing performance
Maximum to minimum ratio – 5 : 1 First to third quartile ratio – 2 : 1 Coefficient of variation – 0.4 by mean approximately Individual differences largely arise from differences
in making and recovering from errors. Error-free expert performance time is much
smaller than above results. Conclusively, Users did not differ much in pure
speed of editing, but differed considerably in time spent making and correcting errors.
How big are Individual Differences in Human-Computer Interaction? Information Search
Maximum to minimum ratio – 9 : 1 vs 3 : 1 First to third quartile ratio – 2 : 1 vs 2 : 1 Coefficient of variation – 0.62 vs 0.3 Differences between 2 types of studies – whether
subjects were required to pursue their searches until the target was found
Also here the differences largely are affected by time spent making and correcting errors.
How big are Individual Differences in Human-Computer Interaction? Programming
Maximum to minimum ratio – 22 : 1 First to third quartile ratio – 3 : 1 Coefficient of variation – 0.75 Also here the differences largely are affected by time spent
making and correcting errors. Argue point – completion time depends on specific
programmer(mental set) x problem(domain knowledge) interactions, that is, it can be major on differences
Coding & debugging times are correlated
How big are Individual Differences in Human-Computer Interaction?
Summary Analyzing the previous results Completion time – positively skewed
distributions little difference between fastest user
and 25%ile user large difference between slowest user
and 75%ile user A large part of the variability is due to
variability in the time taken to recover from errors and to make repeated attempts to solve a problem
Design differences and training differences are smaller than individual differences!!!
What predicts Differences in Performance? Experience Technical aptitude Age Domain specific skills Personality Affective factors
What predicts Differences in Performance? Experience Previous study controls the difference in
experience not estimating experiences When considering experiences - 2 : 1 30 :
1 Gould and Alfaro(1984) In early stage of skill learning, small
differences in the amount of practice can produce large differences in the time to perform the skill.
What predicts Differences in Performance? Technical aptitudes
Spatial aptitude Reasoning aptitude related with mathematics & science Text editing -> more error when spatial memory low case,
deductive reasoning low case Information search -> more error when spatial visualization or
reasoning low case Enginerring major have more performance than humanities and
social science majors. Computer science major needs mathematical aptitude than other
majors -> Programming also is affected by technical aptitude Verbal aptitude are not predicted performance
What predicts Differences in Performance? Technical aptitudes
Spatial ability
Reasoning ability
Evaluate detailed spatial patterns
Locate objects in visual display
Develop strategies
Produce symbolic expressions
What predicts Differences in Performance? Age Also big predictor of performance Aging people have difficulty generating
syntactically complicated commands But Age confounded with experiences!
What predicts Differences in Performance? Domain specific knowledge -> great
difference Personality & Affect -> little difference
Then which predictors make the biggest difference?
Depends on the settings(context?). Always the specific settings are assumed when designing system…
Accommodating User Differences Interface design ( reduce the likelyhood and
severity of user errors )Robust interfacesUser prototypesAdaptive Trainer SystemsAutomated mastery Learning
User training ( anticipate errors and deal with them in a controlled instructional environment )
Accommodating User Differences Robust interfaces Egan and Gomez(1985)’s step to redesign
interfaces Assay user differences Isolate the source of variation Accommodate differences Ex) age is strong predictor -> task simulation reveals
complicated syntax is affected to differences -> redesign simplified command syntax
If user are assumed to be permanent casual user, Robust interface is essential. Ex) ATM
Accommodating User Differences User prototypes Develop a set of user prototypes, classify each user
as one of the prototypes. Ex) flexible vs inflexible text editing system flexible
effective to experienced, inflexible effective to novice Suitable when can categorise the users into several
groups. ex) Language…
Accommodating User Differences Adaptive trainer systems By training, raise performance Prohibit certain types of errors, and give additional
prompting or instruction when errors occur Ex) formatting diskette command : prohibiting the
wrong name Error blocking/Diagnosis/Prompting can be extended
to support the performance of skilled users Suitable for needing to learn a moderate amount to
become productive
Accommodating User Differences Automated ‘Mastery Learning’ Full scale training curriculum High level proficiency to all users Skill is broken down into units & process Each unit instruction is followed by a
diagnostic test Remedial instruction to test results Conventional class vs Mastery learning – 2 :
1 vs 6 : 1 in completion time
Goals in Designing for User Differences Goal #1 : Aid users experiencing greatest
difficulty Best suited to circumstances where a great
variety of people are expected to use a computer system
Goal #2 : Enable users to exploit domain knowledge
Reducing requirements for technical aptitude or specialized skills ( Computer systems are tool!!! )
Goals in Designing for User Differences
Domain knowledge
Systemexperience
Technical Aptitude
Age
Domain knowledge
Systemexperience
Technical Aptitude
A line editorSpeech or handwriting interface