Exploring the design space for adaptive graphical user interfaces
Proceedings of the working conference on Advanced visual interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
User adaptation: good results from poor systems
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proactively Adapting Interfaces to Individual Users for Mobile Devices
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Adaptive web navigation for wireless devices
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Benefits and costs of adaptive user interfaces
International Journal of Human-Computer Studies
Showing user interface adaptivity by animated transitions
Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems
User-Centric Learning and Evaluation of Interactive Segmentation Systems
International Journal of Computer Vision
Minimizing change aversion for the google drive launch
CHI '13 Extended Abstracts on Human Factors in Computing Systems
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Different interfaces allow a user to achieve the same end goal through different action sequences, e.g., command lines vs. drop down menus. Interface efficiency can be described in terms of a cost incurred, e.g., time taken, by the user in typical tasks. Realistic users arrive at evaluations of efficiency, hence making choices about which interface to use, over time, based on trial and error experience. Their choices are also determined by prior experience, which determines how much learning time is required. These factors have substantial effect on the adoption of new interfaces. In this paper, we aim at understanding how users adapt under interface change, how much time it takes them to learn to interact optimally with an interface, and how this learning could be expedited through intermediate interfaces. We present results from a series of experiments that make four main points: (a) different interfaces for accomplishing the same task can elicit significant variability in performance, (b) switching interfaces can result in adverse sharp shifts in performance, (c) subject to some variability, there are individual thresholds on tolerance to this kind of performance degradation with an interface, causing users to potentially abandon what may be a pretty good interface, and (d) our main result -- shaping user learning through the presentation of intermediate interfaces can mitigate the adverse shifts in performance while still enabling the eventual improved performance with the complex interface upon the user becoming suitably accustomed. In our experiments, human users use keyboard based interfaces to navigate a simulated ball through a maze. Our results are a first step towards interface adaptation algorithms that architect choice to accommodate personality traits of realistic users.