Extending Fitts' law to two-dimensional tasks
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A tool for creating predictive performance models from user interface demonstrations
Proceedings of the 12th annual ACM symposium on User interface software and technology
The keystroke-level model for user performance time with interactive systems
Communications of the ACM
Extracting usability information from user interface events
ACM Computing Surveys (CSUR)
The state of the art in automating usability evaluation of user interfaces
ACM Computing Surveys (CSUR)
Predictive human performance modeling made easy
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
VisTrails: visualization meets data management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation
IEEE Transactions on Visualization and Computer Graphics
Toward Cognitive Modeling for Predicting Usability
Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends
EvalBench: a software library for visualization evaluation
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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We present TOME, a novel framework that helps developers quantitatively evaluate user interfaces and design iterations by using histories from crowds of end users. TOME collects user-interaction histories via an interface instrumentation library as end users complete tasks; these histories are compiled using the Keystroke-Level Model (KLM) into task completion-time predictions using CogTool. With many histories, TOME can model prevailing strategies for tasks without needing an HCI specialist to describe users' interaction steps. An unimplemented design change can be evaluated by perturbing a TOME task model in CogTool to reflect the change, giving a new performance prediction. We found that predictions for quick (5-60s) query tasks in an instrumented brain-map interface averaged within 10% of measured expert times. Finally, we modified a TOME model to predict closely the speed-up yielded by a proposed interaction before implementing it.