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 interfaces User prototypes Adaptive Trainer Systems Automated 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 System experience Technical Aptitude Age Domain knowledge System experience Technical Aptitude A line editor Speech or handwriting interface