Modeling task performance for a crowd of users from interaction histories

Steven Gomez, David Laidlaw
2012 Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI '12  
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
more » ... 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.
doi:10.1145/2207676.2208412 dblp:conf/chi/GomezL12 fatcat:wtjwseydcnhdxlgctyyveuwk4y