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Determining and Expressing Interruptibility. Lavar Askew. Interruptibility. Interrupt is the temporary stopping of a task to give attention to another task. Interruptibility reflects how likely an interrupt will affect the completion of the user’s primary task.
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Determining and Expressing Interruptibility Lavar Askew
Interruptibility • Interrupt is the temporary stopping of a task to give attention to another task. • Interruptibility reflects how likely an interrupt will affect the completion of the user’s primary task. • If a user’s interruptibility is high then it is likely that the user may be open to an interruption.
How Do Interruptions Affect Task Performance • Users perform interrupted task 5% - 40% slower non-interrupted task. • Surprisingly, there was no correlation between the quality of completion of the interrupted tasks versus non-interrupted tasks.
Opportune Times for Interruption • Towards the end of the primary task rather than the beginning. • During a temporary break in the execution of the primary task. • Outside of social interaction. • User defined.
Studies • Where are we in terms of developing interruptibility systems? • Look at a couple of studies which will provide some context.
Grapevine Details • System developed by IBM to provide information to “potential communicators to leverage contextual information in making decisions about when to initiate contact and via which channel.” [Too Much Information, Jim Christensen et al.] • A user’s computer, mobile device, telephone and motion detectors were used to provide the contextual information.
What was learned from Grapevine Study? • “Do not expect users to do anything extra to provide context.” • There must be a way to protect user’s privacy. • “A substantial semantic gap exists between the information that low-level sensors and programs can detect and the high-level ability and willingness of a person to communicate with someone else.”
James Fogarty’s work • “Predicting Human Interruptibility with Sensors” • Three key elements to this study: • The human subjects whose actions were record in an office setting. • The human estimators who studied the recordings of the human subjects. • Human coders used to manually simulate sensors.
Human Subjects • “Subjects were prompted for interruptibility self-reports at random, but controlled, intervals averaging two prompts per hour.” • “Subjects were asked to rate your current interruptibility on a five-point scale, with 1 corresponding to ‘Highly Interruptible’ and 5 to ‘Highly Non-Interruptible’.” • “Subjects were present for 672 of these prompts.”
Human Estimators • “[40] estimator subjects were shown portions of the records collected from the [video] subjects.” • Estimator subjects were given 15 – 30 seconds before the video subjects indicated their interruptibility to determine on a scale from 1 to 5 the interruptibility of the video subject.
Results of Human Estimators • In deciding whether the human subject was interruptible on a scale from 1 to 5 the estimators had an overall accuracy of 30.7% • Accuracy when off by 1: 65.8% • Accuracy when deciding between “highly non-interruptible” and all other choices: 76.9% • Chance (always choosing “highly non-interruptible”): 70.6%
Simulated Sensors • “Wizard of Oz” Technique – using humans to simulate sensor behavior. • 24 events were identified by coders. These events were chosen because they were believed to be highly related to interruptibility and physical sensors could easily be built to capture these events.
Sensor Types • Occupant Related Occupant presence. Speaking, writing, sitting, standing, or on the phone Touch of or interaction with: desk, table, file cabinet, food, drink, keyboard, mouse, monitor, and papers. • Guest Related Number of guests present. For each guest: sitting, standing, talking or touching
Sensor Type (Cont.) • Environment Time of the day (hour only). • Aggregate Anybody talk (occupant and guest talking).
Derivations Applied to Sensors • Imm – whether the event occurred in the 15 second interval containing the self-report sample. • All-N – whether event occurred in every 15 second interval during N seconds prior to the sample. • Any-N – whether event occurred in any 15 second interval during N seconds prior to the sample. • Count-N - the number of times the event occurred during intervals in N seconds prior to the sample.
Derivations (cont.) • Change-N - the number of consecutive intervals for which the event occurred in one and did not occur in the other during N seconds prior to the sample. • Net-N - the difference in the sensor between the first interval in N seconds prior to the sample and the sensor in the interval containing the sample.
Feature Set Defined • The combination of sensor types and derivations define our feature set. • Must construct interruptibility model based on this feature set. • Which features are the most effective at predicting interruptibility?
Wrapper-Based Feature Selection Strategy • Start with empty set of features. • Add features to a model to determine which ones most improve the accuracy of the model. • Remove those which do not. • Perform this cycle until there is no change that results in improvement. • Prevents overfitting of data. • Can be slow because this strategy requires repeated application of a machine learning technique to learn which features are most important.
Used 90% of the data for training and 10% for testing. Training resulted in the ten features chosen to represent the model. Model was 82.4% accurate in distinguishing between the human subject being “Highly Non-Interruptible” and all others. Human Estimators:76.9%, Chance: 70.6% Wrapper-Based Feature Selection Using Decision Trees
What’s Been Learned from Fogarty et al. Work • Determining interruptibility based on passive sensors can perform as well or better than humans without user explicitly indicating their interruptibility or interacting with calendars. • Users are most likely “Highly Non-Interruptible” when engaged in a task or a social situation. • In deciding the degree of interruptibility “estimates of 3 or 4 could be used … to initiate a negotiated interruption with an ambient information display.” • “… estimates of 1 or 2 could be used … to decide to initiate with a more direct method.”
Addressing Issues Raised in Grapevine Study • No extra context from users - no user input necessary • Privacy - low-level sensors do not transmit or record data • Short comings of low-level sensors in determining a high-level concept such as interruptibility - use of wrapper-based feature selection and decision trees to infer the degree interruptibility from low-level sensor data.
Interruption Management Systems which Incorporate What’s Been Learned
AuraOrb Details • “AuraOrb uses social awareness cues, such as eye contact to detect user interest in an initially ambient light notification.” • “Once detected, it displays a text message with a notification heading visible from 360 degrees.” • “Touching the orb causes the associated message to be displayed on the user’s computer screen.” • “When user interest is lost, AuraOrb automatically reverts back to its idle state.”
TunnelVision Details • User presses F10 causing the operating system to focus on a single application. • Operating system temporarily suspends desktop notifications while user is in focus. • Bluetooth-enabled desktop connects to user’s Bluetooth-enabled cell phone disabling the ringer and notifying all other Bluetooth devices that user is in a non-interruptible state. • If user goes outside of the 30-foot radius of desktop then the cell phone’s ringer is re-enabled.
Conclusion • Defined interruptibility. • Revealed how interruptions affect task performance. • Provided some socially acceptable opportunities for interruption. • Considered the concerns of those providing the context for interruptibility systems. • Provided some techniques and studies for designing interruptibility systems. • Gave examples of context-aware systems which can infer interruptibility.
