Article

Examining task engagement in sensor-based statistical models of human interruptibility

Authors:

Htet Htet Aung,

Elspeth Golden,

Scott E. HudsonAuthors Info & Claims

CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 331 - 340

https://doi.org/10.1145/1054972.1055018

Published: 02 April 2005 Publication History

Abstract

The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor?based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor?based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low?level input events in a development environment. We then automatically extract features from these low?level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non?interruptible and minimizing cases where the model incorrectly estimates that a programmer is non?interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner.

References

[1]

Anderson, J.R. and Jeffries, R. (1985) Novice LISP Errors: Undetected Losses of Information from Working Memory. Human-Computer Interaction, 1 (2). 107--131.

Digital Library

[2]

Begole, J.B., Tang, J.C. and Hill, R. (2003) Rhythm Modeling, Visualizations, and Applications. Proceedings of the ACM Symposium on User Interface Software and Technology (UIST 2003), 11--20.

Digital Library

[3]

Begole, J.B., Tang, J.C., Smith, R.B. and Yankelovich, N. (2002) Work Rhythms: Analyzing Visualizations of Awareness Histories of Distributed Groups. Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 2002), 334--343.

Digital Library

[4]

Cutrell, E., Czerwinski, M. and Horvitz, E. (2001) Notification, Disruption, and Memory: Effects of Messaging Interruptions on Memory and Performance. Proceedings of the IFIP Conference on Human-Computer Interaction (INTERACT 2001), 263--269.

[5]

Czerwinski, M., Cutrell, E. and Horvitz, E. (2000) Instant Messaging and Interruptions: Influence of Task Type on Performance. Proceedings of the Australian Conference on Computer-Human Interaction (OZCHI 2000), 356--361.

[6]

Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977) Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, 39 (1). 1--38.

[7]

Duda, R.O. and Hart, P.E. (1973) Pattern Classification and Scene Analysis. John Wiley and Sons.

Digital Library

[8]

Fogarty, J., Hudson, S., Atkeson, C., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J. and Yang, J. (2004) Predicting Human Interruptibility with Sensors. To Appear, ACM Transactions on Computer-Human Interaction (TOCHI).

Digital Library

[9]

Fogarty, J., Hudson, S. and Lai, J. (2004) Examining the Robustness of Sensor-Based Statistical Models of Human Interruptibility. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2004), 207--214.

Digital Library

[10]

Gillie, T. and Broadbent, D. (1989) What Makes Interruptions Disruptive? A Study of Length, Similarity, and Complexity. Psychological Research, 50. 243--250.

[11]

Horvitz, E. and Apacible, J. (2003) Learning and Reasoning about Interruption. Proceedings of the International Conference on Multimodal Interfaces (ICMI 2003), 20--27.

Digital Library

[12]

Horvitz, E., Jacobs, A. and Hovel, D. (1999) Attention-Sensitive Alerting. Proceeding of the Conference on Uncertainty and Artificial Intelligence (UAI 1999), 305--313.

Digital Library

[13]

Horvitz, E., Koch, P., Kadie, C.M. and Jacobs, A. (2002) Coordinate: Probabilistic Forecasting of Presence and Availability. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 2002), 224--233.

Digital Library

[14]

Hudson, J.M., Christensen, J., Kellogg, W.A. and Erickson, T. (2002) "I'd be overwhelmed, but it's just one more thing to do": Availability and Interruption in Research Management. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2002), 97--104.

Digital Library

[15]

Hudson, S., Fogarty, J., Atkeson, C., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J. and Yang, J. (2003) Predicting Human Interruptibility with Sensors: A Wizard of Oz Feasibility Study. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2003), 257--264.

Digital Library

[16]

Kern, N. and Schiele, B. (2003) Context-Aware Notification for Wearable Computing. Proceedings of the IEEE International Symposium on Wearable Computing (ISWC 2003).

Digital Library

[17]

Ko, A.J. and Myers, B. (2004) A Framework and Methodology for Studying the Causes of Software Errors in Programming Systems. To Appear, Journal of Visual Languages and Computing.

Digital Library

[18]

Kohavi, R. and John, G.H. (1997) Wrappers for Feature Subset Selection. Artificial Intelligence, 97 (1-2). 273--324.

Digital Library

[19]

Langley, P. and Sage, S. (1994) Induction of Selected Bayesian Classifiers. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI 1994), 399--406.

[20]

Lemaire, P., Abdi, H. and Faylo, M. (1996) The Role of Working Memory Resources in Simple Cognitive Arithmetic. European Journal of Cognitive Psychology, 8 (1). 73--103.

[21]

McFarlane, D.C. (2002) Comparison of Four Primary Methods for Coordinating the Interruption of People in Human-Computer Interaction. Human-Computer Interaction, 17 (1). 63--139.

Digital Library

[22]

Milewski, A.E. and Smith, T.M. (2000) Providing Presence Cues to Telephone Users. Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW 2000), 89--96.

Digital Library

[23]

Perlow, L.A. (1999) The Time Famine: Toward a Sociology of Work Time. Administrative Science Quarterly, 44 (1). 57--81.

[24]

Robertson, T.J., Prabhakararao, S., Burnett, M., Cook, C., Ruthruff, J.R., Beckwith, L. and Phalgune, A. Impact of Interruption Style on End-User Debugging. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2004), ACM Press, 2004, 287--294.

Digital Library

[25]

Seshadri, S. and Shapira, Z. (2001) Managerial Allocation of Time and Effort: The Effects of Interruptions. Management Science, 47 (5). 647--662.

Digital Library

[26]

Witten, I.H. and Frank, E. (1999) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann.

Digital Library

[27]

Yu, L. and Liu, H. (2003) Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. The International Conference on Machine Learning (ICML 2003), 856--863.

Cited By

Chen YWoodward JBista DZhang XSingh IObajemu OShankar MRoss KRuiz JAnthony L(2024)Investigating Contextual Notifications to Drive Self-Monitoring in mHealth Apps for Weight MaintenanceProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641993(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641993
Bardou MLetondal CCausse MImbert JPoirier M(2023)Collaborative Design to Support Mutual Awareness in Single-Pilot Operation and Remote CollaborationProceedings of the 34th Conference on l'Interaction Humain-Machine10.1145/3583961.3583983(1-12)Online publication date: 3-Apr-2023
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Chang XHsu FLiang EChiou ZChuang HTseng FLin YChang Y(2023)Not Merely Deemed as Distraction: Investigating Smartphone Users’ Motivations for Notification-InteractionProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581146(1-17)Online publication date: 19-Apr-2023
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Index Terms

Examining task engagement in sensor-based statistical models of human interruptibility
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

CHI '05: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2005

928 pages

ISBN:1581139985

DOI:10.1145/1054972

Conference Chairs:
Wendy Kellogg
IBM T. J. Watson Research Center
,
Shumin Zhai
IBM Almaden Research Center
,
General Chair:
Gerrit van der Veer
Vrije Universiteit, The Netherlands
,
Program Chair:
Carolyn Gale
Stanford University, USA

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 April 2005

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CHI05: CHI 2005 Conference on Human Factors in Computing Systems

April 2 - 7, 2005

Oregon, Portland, USA

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CHI '05 Paper Acceptance Rate 93 of 372 submissions, 25%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Chen YWoodward JBista DZhang XSingh IObajemu OShankar MRoss KRuiz JAnthony L(2024)Investigating Contextual Notifications to Drive Self-Monitoring in mHealth Apps for Weight MaintenanceProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641993(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641993
Bardou MLetondal CCausse MImbert JPoirier M(2023)Collaborative Design to Support Mutual Awareness in Single-Pilot Operation and Remote CollaborationProceedings of the 34th Conference on l'Interaction Humain-Machine10.1145/3583961.3583983(1-12)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3583961.3583983
Chang XHsu FLiang EChiou ZChuang HTseng FLin YChang Y(2023)Not Merely Deemed as Distraction: Investigating Smartphone Users’ Motivations for Notification-InteractionProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581146(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581146
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Meyer ASatterfield CZuger MKevic KMurphy GZimmermann TFritz T(2022)Detecting Developers’ Task Switches and TypesIEEE Transactions on Software Engineering10.1109/TSE.2020.298408648:1(225-240)Online publication date: 1-Jan-2022
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Mehrotra AMusolesi M(2020)Intelligent Notification SystemsSynthesis Lectures on Mobile and Pervasive Computing10.2200/S00965ED1V01Y201911MPC01411:1(1-75)Online publication date: 3-Jan-2020
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Maddali HLazar ABernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Sociality and Skill Sharing in the GardenProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376246(1-13)Online publication date: 21-Apr-2020
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