Steven Gomez
David Laidlaw
ABSTRACT
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.
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Index Terms
- Modeling task performance for a crowd of users from interaction histories
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