Exploring the design space for adaptive graphical user interfaces
Proceedings of the working conference on Advanced visual interfaces
Automatically generating custom user interfaces for users with physical disabilities
Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
Automatically generating user interfaces adapted to users' motor and vision capabilities
Proceedings of the 20th annual ACM symposium on User interface software and technology
Automatically generating personalized user interfaces
Automatically generating personalized user interfaces
Applied user performance modeling in industry: a case study from medical imaging
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Automatic detection of users' skill levels using high-frequency user interface events
User Modeling and User-Adapted Interaction
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
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The foundation of self-adaptive systems is sound elicitation of the input for the adaptation algorithm. If the input of the adaptation is not reliable, the resulting adaptation will not be reliable either. Especially if the aim is to adapt to the user, the information probably stems from unobtrusive measures but still needs to be reliable. Thus, this paper describes a controlled experiment conducted to investigate in four hypotheses how to make miscellaneous interaction information (which is available anyway) interpretable. These four hypotheses concern three aspects: precision of the interaction step, bias according to right-/left-handedness, and bias of the interaction element. A total of 33 participants were involved. All four hypotheses could be strengthened at a high level of significance.