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Predicting time-sharing in mobile interaction
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DOI：10.1007/s11257-007-9033-x.png)
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摘要
En 中文
The era of modem personal and ubiquitous computers is beset with the problem of fragmentation of the user's time between multiple tasks. Several adaptations have been envisioned that would support the performance of the user in the dynamically changing contexts in which interactions with mobile devices take place. This paper assesses the feasibility of sensor-based prediction of time-sharing, operationalized in terms of the number of glances, the duration of the longest glance, and the total and average durations of the glances to the interaction task. The data used for constructing and validating the predictive models was acquired from a field study (N = 28), in which subjects performing mobile browsing tasks were observed for approximately I h in a variety of environments and situations. The predictive accuracy achieved in binary classification tasks was about 70% (about 20% above default), and the most informative sensors were related to the environment and interactions with the mobile device. Implications to the feasibility of different kinds of adaptations are discussed.
Keyword:
time-sharing
attention
multitasking
interruptions
mobile interaction
mobility classification
predictive models
Bayesian networks
期刊
U
IF:
3.5
论文数:
534
被引数:
1.7K
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