Understanding and Developing Models for Detecting and Differentiating Breakpoints during Task Execution
Year: 2006
Type: report
Abstract: The ability to detect and differentiate breakpoints during task execution is critical for enabling defer-to-breakpoint policies within interruption management. In this work, we examine the feasibility of building models that are capable of detecting and differentiating three granularities (types) of perceptually meaningful breakpoints, without specifications of users. tasks. We collected an ecological sample of task execution data, and then asked human observers to review the collected videos and identify perceived breakpoints and their type. Statistical methods were applied to learn models that map features of the ongoing interaction to each type of breakpoint. Results showed that the models were able to detect and differentiate breakpoints with reasonably high accuracy across tasks. Among many uses, such models can enable interruption management systems to better realize defer-to-breakpoint policies for interactive, free-form tasks.
Authors: Shamsi T. Iqbal, Brian P. Bailey
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W630521606