Abstract
When to send system-mediated interruptions within multitasking environments has been widely debated in the development of interruption management and notification systems primarily from the perspective of single-user interactions. Scholars illustrate that task structure is a useful predictor in determining when to send interruptions. However, these works do not address when to send system interruptions in multi-user, multitasking scenarios and do not address predictors of interruptibility within communication tasks. This paper addresses the issue of predicting interruptibility within multi-user, multitasking communication interactions with special attention to leveraging human interruption techniques as predictors of interruptibility. Specifically, in our project, we will be looking at how task structure and speech information influence human interruption strategies. These strategies could potentially be modeled and integrated into interruption management systems for multi-user, multitasking interactions. We will discuss human interruption strategies and juxtapose them against random interruption strategies, to reveal an intelligent technique for modeling interruptions. We argue that humans use task structure and speech cues to make more informed decisions about when to interrupt that are distinct from more random strategies. An analysis of variance showed that the effect of the interruption strategy (human versus system) on the proximity of an interruption to task boundaries was significant for start and end boundaries, F(2,5938) = 17.46, p = 0.001, F(2,5938) = 7.46, p = 0.006 respectively. This project sheds light on the use of task structure as a predictor of interruptibility within multi-user, multitasking environments via techniques used by human interrupters.
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Peters, N., Romigh, G., Bradley, G., Raj, B. (2018). A Comparative Analysis of Human-Mediated and System-Mediated Interruptions for Multi-user, Multitasking Interactions. In: Nunes, I. (eds) Advances in Human Factors and Systems Interaction. AHFE 2017. Advances in Intelligent Systems and Computing, vol 592. Springer, Cham. https://doi.org/10.1007/978-3-319-60366-7_32
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DOI: https://doi.org/10.1007/978-3-319-60366-7_32
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