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Modeling task performance for a crowd of users from interaction histories
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
doi:10.1145/2207676.2208412
dblp:conf/chi/GomezL12
fatcat:wtjwseydcnhdxlgctyyveuwk4y