Skip to main content
Springer Nature Link
Log in

Context is Key: Mining Social Signals for Automatic Task Detection in Design Thinking Meetings

  • Conference paper
  • First Online:
Design, User Experience, and Usability: UX Research, Design, and Assessment (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13321))

Included in the following conference series:

  • 2461 Accesses

Abstract

Despite the importance of team communication for successful collaborative problem solving, automated solutions for teams are notably absent from the literature. One promising avenue of research has been the development and integration of speech-based technology for team meetings. However, these technologies often fall short of meeting the needs of the teams as they do not take meeting context into consideration. In this paper, we demonstrate the efficacy of context detection with data collected during real team meetings. By capturing and analyzing social signals of rotation in team dynamics, we can demonstrate that different stages of collaborative problem solving using the design thinking methodology differ in their dynamics. Using supervised machine learning, we successfully predict design thinking mode with an overall F1 score of 0.68 and a best-performing sub-class model of 0.94. We believe this to be an essential step towards improving speech-based technology that aims to assist teams during meetings. Making these automated systems context-aware will enable them to provide teams with relevant information, such as resources or guidance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Brown, T., et al.: Design thinking. Harvard Bus. Rev. 86(6), 84 (2008)

    Google Scholar 

  2. Calacci, D., Lederman, O., Shrier, D., Pentland, A.: Breakout: An open measurement and intervention tool for distributed peer learning groups. arXiv preprint arXiv:1607.01443 (2016)

  3. Cawley, G.C., Talbot, N.L.: On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010)

    Google Scholar 

  4. Chandrasegaran, S., Bryan, C., Shidara, H., Chuang, T.Y., Ma, K.L.: Talktraces: Real-time capture and visualization of verbal content in meetings. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2019)

    Google Scholar 

  5. Coutaz, J., Crowley, J.L., Dobson, S., Garlan, D.: Context is key. Comm. ACM 48(3), 49–53 (2005)

    Article  Google Scholar 

  6. Dietzel, J., Francu, R.E., Lucas, B., Zaki, M.: Contextually defined postural markers reveal who’s in charge: Evidence from small teams collected with wearable sensors (2018)

    Google Scholar 

  7. DiMicco, J.M., Pandolfo, A., Bender, W.: Influencing group participation with a shared display. In: Proceedings of the 2004 ACM Conference on Computer Supported Cooperative Work, pp. 614–623 (2004)

    Google Scholar 

  8. Dorst, K.: The core of ‘design thinking’ and its application. Des. Stud. 32(6), 521–532 (2011)

    Article  Google Scholar 

  9. Efeoglu, A., Møller, C., Sérié, M., Boer, H.: Design thinking: Characteristics and promises. In: Proceedings of 14th International CINet Conference on Business Development and Co-creation, pp. 241–256 (2013)

    Google Scholar 

  10. Elsbach, K.D., Stigliani, I.: Design thinking and organizational culture: A review and framework for future research. J. Manage. 44(6), 2274–2306 (2018)

    Google Scholar 

  11. Faucett, H.A., Lee, M.L., Carter, S.: I should listen more: Real-time sensing and feedback of non-verbal communication in video telehealth. Proceedings of the ACM on Human-Computer Interaction 1(CSCW), pp. 1–19 (2017)

    Google Scholar 

  12. Gloor, P.A., Almozlino, A., Inbar, O., Lo, W., Provost, S.: Measuring team creativity through longitudinal social signals. arXiv preprint arXiv:1407.0440 (2014)

  13. Greenberg, S.: Context as a dynamic construct. Hum. Comput. Interact. 16(2–4), 257–268 (2001)

    Article  Google Scholar 

  14. Guilford, J.P.: Creativity. Am. Psychol. 5(9), 444–454 (1950). https://doi.org/10.1037/h0063487

    Article  Google Scholar 

  15. Harrigan, J.A.: Proxemics, kinesics, and gaze. In: The New Handbook of Methods in Nonverbal Behavior Research, pp. 137–198 (2005)

    Google Scholar 

  16. Huber, B., Shieber, S., Gajos, K.Z.: Automatically analyzing brainstorming language behavior with Meeter. In: Proceedings of the ACM on Human-Computer Interaction, vol. 3(CSCW), pp. 1–17 (2019)

    Google Scholar 

  17. Kidane, Y.H., Gloor, P.A.: Correlating temporal communication patterns of the eclipse open source community with performance and creativity. Comput. Math. Organ. Theory 13(1), 17–27 (2007)

    Article  Google Scholar 

  18. Kim, T., Chang, A., Holland, L., Pentland, A.S.: Meeting mediator: Enhancing group collaboration using sociometric feedback. In: Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work, pp. 457–466 (2008)

    Google Scholar 

  19. Kohl, S., Graus, M.P., Lemmink, J.G.: Deciphering the code: Evidence for a sociometric DNA in design thinking meetings. In: International Conference on Human-Computer Interaction, pp. 53–61. Springer (2020)

    Google Scholar 

  20. Liedtka, J.: Perspective: Linking design thinking with innovation outcomes through cognitive bias reduction. J. Prod. Innov. Manage. 32(6), 925–938 (2015)

    Article  Google Scholar 

  21. McGregor, M., Tang, J.C.: More to meetings: Challenges in using speech-based technology to support meetings. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 2208–2220 (2017)

    Google Scholar 

  22. Mumm, J., Mutlu, B.: Human-robot proxemics: Physical and psychological distancing in human-robot interaction. In: Proceedings of the 6th International Conference on Human-Robot Interaction, pp. 331–338. ACM (2011)

    Google Scholar 

  23. Murray, G., Oertel, C.: Predicting group performance in task-based interaction. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 14–20 (2018)

    Google Scholar 

  24. Nathan, M., et al.: In case you missed it: Benefits of attendee-shared annotations for non-attendees of remote meetings. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 339–348 (2012)

    Google Scholar 

  25. Olguın, D.O., Gloor, P.A., Pentland, A.S.: Capturing individual and group behavior with wearable sensors. In: Proceedings of the 2009 AAAI Spring Symposium on Human Behavior Modeling, SSS, vol. 9 (2009)

    Google Scholar 

  26. Olguín, D.O., Waber, B.N., Kim, T., Mohan, A., Ara, K., Pentland, A.: Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics), 39(1), 43–55 (2008)

    Google Scholar 

  27. Olguin Olguin, D.: Sensor-based organizational design and engineering. Ph.D. thesis, Massachusetts Institute of Technology (2011)

    Google Scholar 

  28. Olson, J.S., Teasley, S., Covi, L., Olson, G.: The (currently) unique advantages of collocated work. Distributed work, pp. 113–135 (2002)

    Google Scholar 

  29. Parker, J.N., Cardenas, E., Dorr, A.N., Hackett, E.J.: Using sociometers to advance small group research. Sociol. Methods Res. 0049124118769091 (2018)

    Google Scholar 

  30. Pedregosa, F., et al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    Google Scholar 

  31. Pentland, A.: Honest Signals: How They Shape Our World. MIT Press (2010)

    Google Scholar 

  32. Perry-Smith, J.E., Mannucci, P.V.: From creativity to innovation: The social network drivers of the four phases of the idea journey. Acad. Manage. Rev. 42(1), 53–79 (2017)

    Article  Google Scholar 

  33. Roschelle, J., Teasley, S.D.: The construction of shared knowledge in collaborative problem solving. In: Computer Supported Collaborative Learning, pp. 69–97. Springer (1995)

    Google Scholar 

  34. Schröder, K., Kohl, S.: Vicon - towards understanding visual support systems in collaborative video conferencing. In: International Conference on Human-Computer Interaction, Springer (2022)

    Google Scholar 

  35. Seidel, V.P., Fixson, S.K.: Adopting design thinking in novice multidisciplinary teams: The application and limits of design methods and reflexive practices. J. Prod. Innov. Manage. 30, 19–33 (2013)

    Article  Google Scholar 

  36. Solutions, S.: Sociometric badge 03–02: Preliminary user guide. Boston, MA (2014)

    Google Scholar 

  37. Stempfle, J., Badke-Schaub, P.: Thinking in design teams-an analysis of team communication. Des. Stud. 23(5), 473–496 (2002)

    Article  Google Scholar 

  38. Stewart, A.E., et al.: I say, you say, we say: Using spoken language to model socio-cognitive processes during computer-supported collaborative problem solving. In: Proceedings of the ACM on Human-Computer Interaction, vol. 3(CSCW), pp. 1–19 (2019)

    Google Scholar 

  39. Tripathi, P., Burleson, W.: Predicting creativity in the wild: Experience sample and sociometric modeling of teams. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 1203–1212 (2012)

    Google Scholar 

  40. Wu, L., Waber, B.N., Aral, S., Brynjolfsson, E., Pentland, A.: Mining face-to-face interaction networks using sociometric badges: Predicting productivity in an it configuration task. Available at SSRN 1130251 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Maastricht University, Maastricht, Netherlands

    Steffi Kohl, Mark Graus & Jos G. A. M. Lemmink

  2. Human Data Interaction Lab, Zuyd University of Applied Sciences, Heerlen, Netherlands

    Steffi Kohl

  3. Obvion, Heerlen, Netherlands

    Mark Graus

Authors
  1. Steffi Kohl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Graus
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jos G. A. M. Lemmink
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Steffi Kohl .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology – SUSTech, Shenzhen, China

    Marcelo M. Soares

  2. World Usability Day and Bubble Mountain Consulting, Newton Center, MA, USA

    Elizabeth Rosenzweig

  3. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kohl, S., Graus, M., Lemmink, J.G.A.M. (2022). Context is Key: Mining Social Signals for Automatic Task Detection in Design Thinking Meetings. In: Soares, M.M., Rosenzweig, E., Marcus, A. (eds) Design, User Experience, and Usability: UX Research, Design, and Assessment. HCII 2022. Lecture Notes in Computer Science, vol 13321. Springer, Cham. https://doi.org/10.1007/978-3-031-05897-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-05897-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05896-7

  • Online ISBN: 978-3-031-05897-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics