User Modeling and User-Adapted Interaction

, Volume 17, Issue 5, pp 475–510 | Cite as

Predicting time-sharing in mobile interaction

  • Miikka MiettinenEmail author
  • Antti Oulasvirta
Original Paper


The era of modern 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 1 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.


Time-sharing Attention Multitasking Interruptions Mobile interaction Mobility Classification Predictive models Bayesian networks 


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  1. Adamczyk, P.D., Bailey, B.P.: If not now, when? The effects of interruption at different moments within task execution. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2004), pp. 271–278. ACM Press, New york (2004)Google Scholar
  2. Altmann, E.M., Trafton, J.G.: Task interruption: resumption lag and the role of cues. In: Proceedings of the 26th Annual Conference of the Cognitive Science Society, pp. 42–47. Lawrence Erlbaum Associates, Hillsdale, NJ (2004)Google Scholar
  3. Begole, B., Tang, J., Hill, R.: Rhythm modeling, visualizations and applications. In: Proceedings of the 16th Annual ACM Symposium on User Interface Software and Technology, pp. 11–20. ACM Press, New York (2003)Google Scholar
  4. Burgess P.W. (2000). Strategy application disorder: the role of the frontal lobes in human multitasking. Psychol. Res. 63: 279–288 CrossRefGoogle Scholar
  5. Carberry S. (2001). Techniques for plan recognition. User Model. User-Adapt. Interact. 11(1–2): 31–48 zbMATHCrossRefGoogle Scholar
  6. Card, S.K., Henderson, A.: A multiple, virtual-workspace interface to support user task switching. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 1987), pp. 53–59. ACM Press, New York (1987)Google Scholar
  7. Cutrell, E., Czerwinski, M., Horvitz, E.: Notification, disruption, and memory: effects of messaging interruptions on memory and performance. In: Proceedings of Interact 2001: IFIP Conference on Human-Computer Interaction, pp. 263–269. IOS Press, Amsterdam (2001)Google Scholar
  8. Dourish, P., Bellotti, V.: Awareness and coordination in shared workspaces. In: Proceedings of the ACM Conference on Computer-Supported Cooperative Work (CSCW 1992), pp. 107–114. ACM Press, New York (1992)Google Scholar
  9. Eng, K., Lewis, R.L., Tollinger, I., Chu, A., Howes, A., Vera, A.: Generating automated predictions of behavior strategically adapted to specific performance objectives. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2006), pp. 621–630. ACM Press, New York (2006)Google Scholar
  10. Fischer G. (2001). User modeling in human-computer interaction. User Model. User-Adapt. Interact. 11(1): 65–86 zbMATHCrossRefGoogle Scholar
  11. Fogarty, J., Ko, A.J., Aung, H.H., Golden, E., Tang, K.P., Hudson, S.E.: Examining task engagement in sensor-based statistical models of human interruptibility. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2005), pp. 331–340. ACM Press, New York (2005)Google Scholar
  12. Fogarty, J., Lai, J.: Examining the robustness of statistical models. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2004), pp. 207–214. ACM Press, New York (2004)Google Scholar
  13. Fogarty J., Lai J. and Christensen J. (2004). Presence versus availability: the design and evaluation of a context-aware communication client. Int. J. Hum.-Comput. Stud. 61(3): 299–317 CrossRefGoogle Scholar
  14. Fu W.T. and Gray W.D. (2004). Resolving the paradox of the active user: stable suboptimal performance in interactive tasks. Cogn. Sci. 28(6): 901–935 CrossRefGoogle Scholar
  15. Glanzer M., Dorfman D. and Kaplan B. (1981). Short-term processing in the processing of text. J. Verbal Learn. Verbal Behav. 20: 656–670 CrossRefGoogle Scholar
  16. González, V.M, Mark G.: Constant, constant, multi-tasking craziness: managing multiple working spheres. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2004), pp. 113–120. ACM Press, New York (2004)Google Scholar
  17. Gray W.D. and Boehm-Davis D.A. (2000). Milliseconds matter: an introduction to microstrategies and to their use in describing and predicting interactive behavior. J. Exp. Psychol. Appl. 6(4): 322–335 CrossRefGoogle Scholar
  18. Guyon I. and Elisseeff A. (2003). An introduction to variable and feature selection. J. Mach. Learn. Res. 3: 1157–1182 zbMATHCrossRefGoogle Scholar
  19. Hjorth J. (1994). Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap. Chapman & Hall, London zbMATHGoogle Scholar
  20. Ho, J., Intille, S.S.: Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. In: Proceedings the ACM Conference on Human Factors in Computing Systems (CHI 2005), pp. 909–918. ACM Press, New York (2005)Google Scholar
  21. Horvitz, E.: Principles of mixed-initiative user interfaces. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 1999), pp. 159–166. ACM Press, New York (1999a)Google Scholar
  22. Horvitz E. (1999). Uncertainty, action and interaction: in pursuit of mixed-initiative computing. IEEE Intell. Syst. 14(5): 17–20 Google Scholar
  23. Horvitz, E., Apacible, J.: Learning and reasoning about interruption. In: Proceedings of the Fifth International Conference on Multimodal Interfaces (ICMI 2003), pp. 20–27. ACM Press, New York (2003)Google Scholar
  24. Horvitz, E., Jacobs, A., Hovel, D.: Attention-sensitive alerting. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI’99), pp. 305–313. Morgan Kaufmann, San Francisco, CA (1999)Google Scholar
  25. Horvitz E., Kadie C.M., Paek T. and Hovel D. (2003). Models of attention in computing and communications: from principles to applications. Commun. ACM 46(3): 52–59 CrossRefGoogle Scholar
  26. Hudson, S., Fogarty, J., Atkeson, C., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J., Yang, J.: Predicting human interruptibility with sensors: a wizard of oz feasibility study. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2003), pp. 257–264. ACM Press, New York (2003)Google Scholar
  27. Jameson, A., Kiefer, J., Mueller, C., Grossmann-Hutter, B., Wittig, F., Rummer, R.: Assessment of a user’s time pressure and cognitive load on the basis of features of speech. Technical report, German Research Institute for Artificial Intelligence, Saarbrücken, Germany (2006)Google Scholar
  28. Jameson A. and Klöckner K. (2005). User multitasking with mobile multimodal systems. In: Minker, W., Bühler, D. and Dybkjær, L. (eds) Spoken Multimodal Human-Computer Dialogue in Mobile Environments, pp 349–377. Springer, Dordrecht CrossRefGoogle Scholar
  29. Jameson A., Schaefer R., Weis T., Berthold A. and Weyrath T. (1999). Making systems sensitive to the user’s changing resource limitations. Knowledge-Based Syst 12: 413–425 CrossRefGoogle Scholar
  30. Kern, N., Antifakos, S., Schiele, B., Schwaninger, A.: A model for human interruptability: experimental evaluation and automatic estimation from wearable sensors. In: Proceedings of the Eighth International Symposium on Wearable Computers (ISWC’04), pp. 158–165. IEEE Computer Society, Washington, DC (2004)Google Scholar
  31. Kern, N., Schiele B.: Context-aware notification for wearable computing. In: Proceedings of the 7th IEEE International Symposium on Wearable Computers (ISWC’03), pp. 223–230. IEEE Computer Society, Washington, DC (2003)Google Scholar
  32. Kobsa A. (2001). Generic user modeling systems. User Model. User-Adap. Interact. 11(1–2): 49–63 zbMATHCrossRefGoogle Scholar
  33. Kohavi R. and John G. (1997). Wrappers for feature selection. Artif. Intell. 97(1–2): 273–324 zbMATHCrossRefGoogle Scholar
  34. Kushleyeva Y., Salvucci D. and Lee F.J. (2005). Deciding when to switch tasks in time-critical multitasking. Cogn. Syst. Res. 6: 41–49 CrossRefGoogle Scholar
  35. Mäntyjärvi J. and Seppänen T. (2003). Adapting applications in handheld devices using fuzzy context information. Interact. Comput. 15(4): 521–538 CrossRefGoogle Scholar
  36. McFarlane D.C. and Latorella K.A. (2002). The scope and importance of human interruption in human-computer interaction design. Hum. Comput. Interact. 17(1): 1–61 CrossRefGoogle Scholar
  37. Miyata Y. and Norman D.A. (1986). Psychological issues in supporting multiple activities. In: Norman, D.A. and Draper, S.W (eds) User Centered Design: New Perspectives on Human-Computer Interaction, pp 266–284. Lawrence Erlbaum Associates, Hillsdale, NJ Google Scholar
  38. Monsell S. (2003). Task switching. Trends Cogn. Sci. 7(3): 134–140 CrossRefGoogle Scholar
  39. Näätänen R. (1992). Attention and Brain Function. Lawrence Erlbaum Associates, Hillsdale, NJ Google Scholar
  40. Oulasvirta A., Petit R., Raento M. and Tiitta S. (2007). Interpreting and acting on mobile awareness cues. Hum.-Comput. Interact. 22(1&2): 97–135 Google Scholar
  41. Oulasvirta, A., Tamminen, S., Roto, V., Kuorelahti, J.: Interaction in 4-second bursts: the fragmented nature of attentional resources in mobile HCI. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2005), pp. 919–928, ACM Press, New York (2005)Google Scholar
  42. Pashler H. (1993). Dual-task interference and elementary mental mechanisms. In: Meyer, D. and Kornblum, S. (eds) Attention and Performance XIV, pp 245–264. MIT Press, Cambridge, MA Google Scholar
  43. Russell S. and Norvig P. (2003). Artificial Intelligence: A Modern Approach (2nd edn). Pearson Education, Upper Saddle River, NJ Google Scholar
  44. Salovaara, A., Oulasvirta, A.: Six modes of proactive resource management. In: Proceedings of NordiCHI 2004, pp. 57–60. ACM Press, New York (2004)Google Scholar
  45. Salvucci D. (2005). A multitasking general executive for compound continuous tasks. Cogn. Sci. 29: 257–292 Google Scholar
  46. Simon H. (1971). Designing organizations for an information rich world. In: Greenberger, M. (eds) Computers, Communications and the Public Interest, pp 37–72. Johns Hopkins University Press, Baltimore, MD Google Scholar
  47. Tamminen S., Oulasvirta A., Toiskallio K. and Kankainen A. (2004). Understanding mobile contexts. Pers. Ubiquitous Comput. 8(2): 135–143 CrossRefGoogle Scholar
  48. Vera, A., Howes, A., McCurdy, M., Lewis, R.L.: A constraint satisfaction approach to predicting skilled interactive cognition. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2004), pp. 121–128. ACM Press, New York (2004)Google Scholar
  49. Vertegaal R. (2003). Attentive user interfaces. Commun. ACM 46(3): 31–33 CrossRefGoogle Scholar
  50. Wickens C.D. (1984). Processing resources in attention. In: Parasuraman, R. and Davies, R. (eds) Varieties of Attention, pp 63–102. Academic Press, New York Google Scholar
  51. Wickens C.D. (2002). Multiple resources and performance prediction’. Theor. Issues Ergon. Sci. 3(2): 159–177 CrossRefGoogle Scholar
  52. Wikman A.S., Nieminen T. and Summala H. (1998). Driving experience and time-sharing during in-car tasks on roads of different width. Ergonomics 41: 358–372 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Helsinki Institute for Information Technology (HIIT)University of Helsinki and Helsinki University of TechnologyEspooFinland

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