A Research Framework for the Smartphone-Based Contextual Study of Mobile Knowledge Work

  • Mikko Heiskala
  • Eero Palomäki
  • Matti Vartiainen
  • Kai Hakkarainen
  • Hanni Muukkonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8518)


We present an initial research framework for the contextual study of mobile knowledge work that combines automatic, objective data collection from smartphone sensors with subjective participant self-reported data possibly complemented with researcher conducted interviews. The framework shows how raw sensor data, contextual information inferred from the sensor data, both in real-time and post hoc, can be used in tandem with smartphone administered questionnaires and post hoc in-depth interviews to study mobile knowledge work. We evaluate the framework by reporting some early experiences from a pilot study of mobile knowledge work.


mobile sensing mobile data collection mobile knowledge work context-awareness smartphone-based research 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Verkasalo, H.: Contextual patterns in mobile service usage. Personal and Ubiquitous Computing 13(5), 331–342 (2009)CrossRefGoogle Scholar
  2. 2.
    Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Communications Magazine 48(9), 140–150 (2010)CrossRefGoogle Scholar
  3. 3.
    Khan, W., Xiang, Y., Aalsalem, M., Arshad, Q.: Mobile Phone Sensing Systems: A Survey. IEEE Communications Surveys & Tutorials 15(1), 402–427 (2012)CrossRefGoogle Scholar
  4. 4.
    Soikkeli, T., Karikoski, J., Hämmäinen, H.: An end-user context framework for handset-based studies. In: The 19th ITS Biennial Conference, Bangkok, Thailand, pp. 18–21 (2012)Google Scholar
  5. 5.
    Bouwman, H., de Reuver, M., Heerschap, N., Verkasalo, H.: Opportunities and problems with automated data collection via smartphones. Mobile Media & Communication 1(1), 63–68 (2013)CrossRefGoogle Scholar
  6. 6.
    Eagle, N.: Mobile phones as sensors for social research. In: Hesse-Biber, S.N. (ed.) Emergent Technologies in Social Research, pp. 492–521. Oxford University Press, New York (2011)Google Scholar
  7. 7.
    Intille, S.S.: Emerging Technology for Studying Daily Life. In: Mehl, M.R., Conner, T.S. (eds.) Handbook of Research Methods for Studying Daily Life, pp. 267–282. Guilford Press (2011)Google Scholar
  8. 8.
    Muukkonen, H., Inkinen, M., Kosonen, K., Hakkarainen, K., Karlgren, K., Lachmann, H., Vesikivi, P.: Research on knowledge practices with the Contextual Activity Sampling System. In: Proc. of the 9th Int.Conf. of Computer Supported Collaborative Learning, Rhodes, Greece, vol. 1, pp. 385–394. International Society of the Learning Sciences (2009)Google Scholar
  9. 9.
    Miller, G.: The smartphone psychology manifesto. Perspectives on Psychological Science 7(3), 221–237 (2012)CrossRefGoogle Scholar
  10. 10.
    Rachuri, K.K., Musolesi, M., Mascolo, C., Rentfrow, P.J., Longworth, C., Aucinas, A.: EmotionSense: a mobile phones based adaptive platform for experimental social psychology research. In: 12th ACM Int.Conf. on Ubiquitous Computing, pp. 281–290 (2010)Google Scholar
  11. 11.
    Dey, A.K.: Understanding and using context. Personal and Ubiquitous Computing 5(1), 4–7 (2001)CrossRefGoogle Scholar
  12. 12.
    Intille, S.S.: Technological innovations enabling automatic, context-sensitive ecological momentary assessment. In: Stone, A.A., Shiffman, S., Atienza, A.A., Nebeling, L. (eds.) The Science of Real-time Data Capture: Self-reports in Health Research, pp. 308–337. Oxford University Press, New York (2007)Google Scholar
  13. 13.
    Kahneman, D., Krueger, A.B., Schkade, D.A., Schwarz, N., Stone, A.A.: A survey method for characterizing daily life experience: The day reconstruction method. Science 306(5702), 1776–1780 (2004)CrossRefGoogle Scholar
  14. 14.
    Muukkonen, H., Hakkarainen, K., Inkinen, M., Lonka, K., Salmela-Aro, K.: CASS-methods and tools for investigating higher education knowledge practices. In: Kanselaar, G., Jonker, V., Kirschner, P., Prins, F. (eds.) Int. Conf. for the Learning Sciences (ICLS 2008), vol. 2, pp. 107–115 (2008)Google Scholar
  15. 15.
    Mannonen, P., Karhu, K., Heiskala, M.: An approach for understanding personal mobile ecosystem in everyday context. In: Li, H., Järveläinen, J. (eds.) Effective, Agile, and Trusted eServices Co-Creation, 15th International Conference on Electronic Commerce (ICEC 2013). TUCS Lecture Notes, No 19, pp. 135–146 (August 2013)Google Scholar
  16. 16.
    Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A.: Social fMRI: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing 7(6), 643–659 (2011)CrossRefGoogle Scholar
  17. 17.
    Hey, T., Tansley, S., Tolle, K.: The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research (2009)Google Scholar
  18. 18.
    Nusser, S.M., Intelle, S., Maitra, R.: Emerging technologies and next-generation intensive longitudinal data collection. In: Walls, T.A., Schafer, J.L. (eds.) Models for Intensive Longitudinal Data, pp. 254–278. Oxford University Press, New York (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mikko Heiskala
    • 1
  • Eero Palomäki
    • 2
  • Matti Vartiainen
    • 2
  • Kai Hakkarainen
    • 3
  • Hanni Muukkonen
    • 4
  1. 1.School of Science, Department of Computer Science and EngineeringAalto UniversityHelsinkiFinland
  2. 2.School of Science, Department of Industrial Engineering and Management, Work Psychology and LeadershipAalto UniversityHelsinkiFinland
  3. 3.Department of EducationUniversity of TurkuTurkuFinland
  4. 4.Faculty of Agriculture and ForestryUniversity of HelsinkiHelsinkiFinland

Personalised recommendations