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Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Supporting cognitive models as users
ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 2
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
Effects of instant messaging interruptions on computing tasks
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Predicting human interruptibility with sensors: a Wizard of Oz feasibility study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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International Journal of Human-Computer Studies - Notification user interfaces
Learning and reasoning about interruption
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A diary study of task switching and interruptions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Examining the robustness of sensor-based statistical models of human interruptibility
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
BusyBody: creating and fielding personalized models of the cost of interruption
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Examining task engagement in sensor-based statistical models of human interruptibility
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CHI '05 Extended Abstracts on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM Transactions on Computer-Human Interaction (TOCHI)
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the 2010 ACM Symposium on Applied Computing
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ACM Transactions on Computer-Human Interaction (TOCHI)
Study of user interruptibility estimation based on focused application switching
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Interaction mediate agent based on user interruptibility estimation
HI'11 Proceedings of the 2011 international conference on Human interface and the management of information - Volume Part I
Preliminary findings of visualization of the interruptible moment
HPCS'09 Proceedings of the 23rd international conference on High Performance Computing Systems and Applications
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Investigating interruptions in the context of computerised cognitive testing for older adults
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Modeling information exchange opportunities for effective human-computer teamwork
Artificial Intelligence
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ACM Transactions on Computer-Human Interaction (TOCHI)
Estimation of interruptibility during office work based on PC activity and conversation
HCI'13 Proceedings of the 15th international conference on Human Interface and the Management of Information: information and interaction for learning, culture, collaboration and business - Volume Part III
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A challenge in building interruption reasoning systems is to compute an accurate cost of interruption (COI). Prior work has used interface events and other cues to predict COI, but ignore characteristics related to the structure of a task. This work investigates how well characteristics of task structure can predict COI, as objectively measured by resumption lag. In an experiment, users were interrupted during task execution at various boundaries to collect a large sample of resumption lag values. Statistical methods were employed to create a parsimonious model that uses characteristics of task structure to predict COI. A subsequent experiment with different tasks showed that the model can predict COI with reasonably high accuracy. Our model can be expediently applied to many goal-directed tasks, allowing systems to make more effective decisions about when to interrupt.