Automated Usability Evaluation of Model-Based Adaptive User Interfaces for Users with Special and Specific Needs by Simulating User Interaction

2013 ◽  
pp. 219-247 ◽  
Author(s):  
Michael Quade ◽  
Grzegorz Lehmann ◽  
Klaus-Peter Engelbrecht ◽  
Dirk Roscher ◽  
Sahin Albayrak
Keyword(s):  
User Interfaces ◽  
User Interaction ◽  
Usability Evaluation ◽  
Model Based ◽  
Adaptive User Interfaces ◽  
Automated Usability Evaluation

scholarly journals Automated Usability Evaluation during Model-Based Interactive System Development

2008 ◽  
pp. 134-141 ◽  
Author(s):  
Sebastian Feuerstack ◽  
Marco Blumendorf ◽  
Maximilian Kern ◽  
Michael Kruppa ◽  
Michael Quade ◽  
...  
Keyword(s):  
System Development ◽  
Usability Evaluation ◽  
Interactive System ◽  
Model Based ◽  
Automated Usability Evaluation

scholarly journals Model-Based Design of Adaptive User Interfaces through Connectors

2003 ◽  
pp. 245-257 ◽  
Author(s):  
Víctor López-Jaquero ◽  
Francisco Montero ◽  
José P. Molina ◽  
Antonio Fernández-Caballero ◽  
Pascual González
Keyword(s):  
User Interfaces ◽  
Model Based ◽  
Adaptive User Interfaces

Classification of Attentional Tunneling Through Behavioral Indices

2019 ◽  
Vol 62 (6) ◽  
pp. 973-986 ◽  
Author(s):  
Sean W. Kortschot ◽  
Greg A. Jamieson
Keyword(s):  
Machine Learning ◽  
User Interfaces ◽  
Adaptive Systems ◽  
User Interaction ◽  
Adaptive Automation ◽  
Behavioral Indices ◽  
Learning Classifier ◽  
Adaptive User Interfaces ◽  
Trade Offs

Objective The objective of this study was to develop a machine learning classifier to infer attentional tunneling through behavioral indices. This research serves as a proof of concept for a method for inferring operator state to trigger adaptations to user interfaces. Background Adaptive user interfaces adapt their information content or configuration to changes in operating context. Operator attentional states represent a promising class of triggers for these adaptations. Behavioral indices may be a viable alternative to physiological correlates for triggering interface adaptations based on attentional state. Method A visual search task sought to induce attentional tunneling in participants. We analyzed user interaction under tunnel and non-tunnel conditions to determine whether the paradigm was successful. We then examined the performance trade-offs stemming from attentional tunnels. Finally, we developed a machine learning classifier to identify patterns of interaction characteristics associated with attentional tunnels. Results The experimental paradigm successfully induced attentional tunnels. Attentional tunnels were shown to improve performance when information appeared within them, but to hinder performance when it appeared outside. Participants were found to be more tunneled in their second tunnel trial relative to their first. Our classifier achieved a classification accuracy similar to comparable studies (area under curve = 0.74). Conclusion Behavioral indices can be used to infer attentional tunneling. There is a performance trade-off from attentional tunneling, suggesting the opportunity for adaptive systems. Application This research applies to adaptive automation aimed at managing operator attention in information-dense work domains.

scholarly journals Using HCI Patterns within the Model-Based Development of Run-Time Adaptive User Interfaces

2010 ◽  
Vol 43 (13) ◽  
pp. 477-482 ◽  
Author(s):  
Marc Seissler ◽  
Gerrit Meixner ◽  
Kai Breiner
Keyword(s):  
User Interfaces ◽  
Model Based ◽  
Adaptive User Interfaces ◽  
Run Time

scholarly journals Adaptive User Interfaces for Smart Environments with the Support of Model-Based Languages

2012 ◽  
pp. 33-48 ◽  
Author(s):  
Sara Bongartz ◽  
Yucheng Jin ◽  
Fabio Paternò ◽  
Joerg Rett ◽  
Carmen Santoro ◽  
...  
Keyword(s):  
User Interfaces ◽  
Smart Environments ◽  
Model Based ◽  
Adaptive User Interfaces
Sign in / Sign up

Export Citation Format

Share Document