Shamsi T. Iqbal
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Publications by Shamsi T. Iqbal (bibliography)
» 2008 «
Iqbal, Shamsi T. and Bailey, Brian P. (2008): Effects of intelligent notification management on users and their tasks. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 93-102. Available online
“We present a novel system for notification management and report results from two studies testing its performance and impact. The system uses statistical models to realize defer-to-breakpoint policies for managing notifications. The first study tested how well the models detect three types of breakpoints within novel task sequences. Results show that the models detect breakpoints reasonably well, but struggle to differentiate their type. Our second study explored effects of managing notifications with our system on users and their tasks. Results showed that scheduling notifications at breakpoints reduces frustration and reaction time relative to delivering them immediately. We also found that the relevance of notification content determines the type of breakpoint at which it should be delivered. The core concept of scheduling notifications at breakpoints fits well with how users prefer notifications to be managed. This indicates that users would likely adopt the use of notification management systems in practice.”
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» 2007 «
Iqbal, Shamsi T. and Horvitz, Eric (2007): Disruption and recovery of computing tasks: field study, analysis, and directions. In: Proceedings of ACM CHI 2007 Conference on Human Factors in Computing Systems 2007. pp. 677-686. Available online
“We report on a field study of the multitasking behavior of computer users focused on the suspension and resumption of tasks. Data was collected with a tool that logged users' interactions with software applications and their associated windows, as well as incoming instant messaging and email alerts. We describe methods, summarize results, and discuss design guidelines suggested by the findings.”
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Iqbal, Shamsi T. and Bailey, Brian P. (2007): Understanding and developing models for detecting and differentiating breakpoints during interactive tasks. In: Proceedings of ACM CHI 2007 Conference on Human Factors in Computing Systems 2007. pp. 697-706. Available online
“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 statistical models that can detect and differentiate three granularities (types) of perceptually meaningful breakpoints during task execution, without having to recognize the underlying tasks. We collected ecological samples of task execution data, and asked observers to review the interaction in the collected videos and identify any perceived breakpoints and their type. Statistical methods were applied to learn models that map features of the 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, our resulting models can enable interruption management systems to better realize defer-to-breakpoint policies for interactive, free-form tasks.”
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Bailey, Brian P., Busbey, Chris W. and Iqbal, Shamsi T. (2007): TAPRAV: An interactive analysis tool for exploring workload aligned to models of task execution. In Interacting with Computers, 19 (3) pp. 314-329
“Pupillary response is a valid indicator of mental workload and is being increasingly leveraged to identify lower cost moments for interruption, evaluate complex interfaces, and develop further understanding of psychological processes. Existing tools are not sufficient for analyzing this type of data, as it typically needs to be analyzed in relation to the corresponding task's execution. To address this emerging need, we have developed a new interactive analysis tool, TAPRAV. The primary components of the tool include; (i) a visualization of pupillary response aligned to the corresponding model of task execution, useful for exploring relationships between these two data sources; (ii) an interactive overview + detail metaphor, enabling rapid inspection of details while maintaining global context; (iii) synchronized playback of the video of the user's screen interaction, providing awareness of the state of the task; and (iv) interaction supporting discovery driven analysis. Results from a user study showed that users are able to efficiently interact with the tool to analyze relationships between pupillary response and task execution. The primary contribution of our tool is that it demonstrates an effective visualization and interaction design for rapidly exploring pupillary response in relation to models of task execution, thereby reducing the analysis effort.”
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Bailey, Brian P. and Iqbal, Shamsi T. (2007): Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. In ACM Transactions on Computer-Human Interaction, 14 (4) p. 21
“Notifications can have reduced interruption cost if delivered at moments of lower mental workload during task execution. Cognitive theorists have speculated that these moments occur at subtask boundaries. In this article, we empirically test this speculation by examining how workload changes during execution of goal-directed tasks, focusing on regions between adjacent chunks within the tasks, that is, the subtask boundaries. In a controlled experiment, users performed several interactive tasks while their pupil dilation, a reliable measure of workload, was continuously measured using an eye tracking system. The workload data was extracted from the pupil data, precisely aligned to the corresponding task models, and analyzed. Our principal findings include (i) workload changes throughout the execution of goal-directed tasks; (ii) workload exhibits transient decreases at subtask boundaries relative to the preceding subtasks; (iii) the amount of decrease tends to be greater at boundaries corresponding to the completion of larger chunks of the task; and (iv) different types of subtasks induce different amounts of workload. We situate these findings within resource theories of attention and discuss important implications for interruption management systems.”
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Iqbal, Shamsi T. and Horvitz, Eric (2007): Conversations Amidst Computing: A Study of Interruptions and Recovery of Task Activity. In: Conati, Cristina, McCoy, Kathleen F. and Paliouras, Georgios (eds.) User Modeling 2007 - 11th International Conference - UM 2007 June 25-29, 2007, Corfu, Greece. pp. 350-354. Available online
» 2006 «
Iqbal, Shamsi T. and Bailey, Brian P. (2006): Leveraging characteristics of task structure to predict the cost of interruption. In: Proceedings of ACM CHI 2006 Conference on Human Factors in Computing Systems 2006. pp. 741-750. Available online
“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.”
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» 2005 «
Iqbal, Shamsi T., Adamczyk, Piotr D., Zheng, Xianjun Sam and Bailey, Brian P. (2005): Towards an index of opportunity: understanding changes in mental workload during task execution. In: Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems 2005. pp. 311-320. Available online
“To contribute to systems that reason about human attention, our work empirically demonstrates how a user's mental workload changes during task execution. We conducted a study where users performed interactive, hierarchical tasks while mental workload was measured through the use of pupil size. Results show that (i) different types of subtasks impose different mental workload, (ii) workload decreases at subtask boundaries, (iii) workload decreases more at boundaries higher in a task model and less at boundaries lower in the model, (iv) workload changes among subtask boundaries within the same level of a task model, and (v) effective understanding of why changes in workload occur requires that the measure be tightly coupled to a validated task model. From the results, we show how to map mental workload onto a computational Index of Opportunity that systems can use to better reason about human attention.”
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Iqbal, Shamsi T. and Bailey, Brian P. (2005): Investigating the effectiveness of mental workload as a predictor of opportune moments for interruption. In: Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems 2005. pp. 1489-1492. Available online
“This work investigates the use of workload-aligned task models for predicting opportune moments for interruption. From models for several tasks, we selected boundaries with the lowest (Best) and highest (Worst) mental workload. We compared effects of interrupting primary tasks at these and Random moments on resumption lag, annoyance, and social attribution. Results show that interrupting at the Best moments consistently caused less resumption lag and annoyance, and fostered more social attribution. Results demonstrate that use of workload-aligned models offers a systematic method for predicting opportune moments.”
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» 2004 «
Iqbal, Shamsi T., Zheng, Xianjun Sam and Bailey, Brian B. (2004): Task-Evoked Pupillary Response to Mental Workload in Human-Computer Interaction. In: Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems April 24-29, 2004, Vienna, Austria. pp. 1477-1480. Available online
“Accurate assessment of a user's mental workload will be critical for developing systems that manage user attention (interruptions) in the user interface. Empirical evidence suggests that an interruption is much less disruptive when it occurs during a period of lower mental workload. To provide a measure of mental workload for interactive tasks, we investigated the use of task-evoked pupillary response. Results show that a more difficult task demands longer processing time, induces higher subjective ratings of mental workload, and reliably evokes greater pupillary response at salient subtasks. We discuss the findings and their implications for the design of an attention manager.”
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Mar 20th, 2010
Changes to this page (author)
20 Feb 2010: Enabled abstracts to be shown on Shamsi T. Iqbal's author page.30 May 2009: Author was edited 12 May 2008: Author was edited
12 May 2008: Author was edited
12 May 2008: Author was edited
16 Jul 2007: Conference Article was added to the page (approved by an editor)
16 Jul 2007: Author was added to the bibliography (approved by an editor)
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19 Jun 2007: Author was edited
19 Jun 2007: Author was added to the bibliography
