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Computationally Augmented Ethnography: Emotion Tracking and Learning in Museum Games

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Advances in Quantitative Ethnography (ICQE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1112))

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Abstract

In this paper, we describe a way of using multi-modal learning analytics to augment qualitative data. We extract facial expressions that may indicate particular emotions from videos of dyads playing an interactive table-top game built for a museum. From this data, we explore the correlation between students’ understanding of the biological and complex systems concepts showcased in the learning environment and their facial expressions. First, we show how information retrieval techniques can be used on facial expression features to investigate emotional variation during key moments of the interaction. Second, we connect these features to moments of learning identified by traditional qualitative methods. Finally, we present an initial pilot using these methods in concert to identify key moments in multiple modalities. We end with a discussion of our preliminary findings on interweaving machine and human analytical approaches.

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References

  1. Blikstein, P.: An atom is known by the company it keeps: a constructionist learning environment for materials science using multi-agent simulation (2006)

    Google Scholar 

  2. Chi, M.T.H., Roscoe, R.D., Slotta, J.D., Roy, M., Chase, C.C.: Misconceived causal explanations for emergent processes. Cogn. Sci. 36(1), 1–61 (2012). https://doi.org/10.1111/j.1551-6709.2011.01207.x. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1551-6709.2011.01207.x

    Article  Google Scholar 

  3. Council, N.R.: Learning Science in Informal Environments: People, Places, and Pursuits. National Academies Press (2009)

    Google Scholar 

  4. D’Mello, S., Dieterle, E., Duckworth, A.: Advanced, analytic, automated (AAA) measurement of engagement during learning. Educ. Psychol. 52(2), 104–123 (2017). https://doi.org/10.1080/00461520.2017.1281747

    Article  Google Scholar 

  5. D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instr. 22(2), 145–157 (2012). https://doi.org/10.1016/j.learninstruc.2011.10.001. http://www.sciencedirect.com/science/article/pii/S0959475211000806

    Article  Google Scholar 

  6. Falk, J.H., Dierking, L.D.: The Museum Experience Revisited. Routledge (2016)

    Google Scholar 

  7. Gregory, J.: Game Engine Architecture. AK Peters/CRC Press (2017)

    Google Scholar 

  8. Martin, K., Wilensky, U.: Netlogo ant adapatation model (2019). http://ccl.northwestern.edu/netlogo/models/AntAdaptation

  9. Martin, K.: Constructivist dialogue mapping: evaluating learning during play of ant adaptation, a complex interactive tabletop museum game. In: 31st Annual Visitor Studies Association, p. 20 (2018)

    Google Scholar 

  10. Martin, K.: Multitouch NetLogo for museum interactive game. In: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2018, Jersey City, NJ, USA, pp. 5–8. ACM, New York (2018). https://doi.org/10.1145/3272973.3272989. http://doi.acm.org/10.1145/3272973.3272989

  11. Martin, K., Horn, M., Wilensky, U.: Ant Adaptation: A Complex Interactive Multitouch Game about Ants Designed for Museums, August 2018

    Google Scholar 

  12. Martin, K., Horn, M., Wilensky, U.: Prevalence of direct and emergent schema and change after play. Inf. Educ. 18(1), 183 (2019)

    Google Scholar 

  13. McGaugh, J.L.: Memory and Emotion: The Making of Lasting Memories. Columbia University Press (2003)

    Google Scholar 

  14. McGaugh, J.L.: Make mild moments memorable: add a little arousal. Trends Cogn. Sci. 10(8), 345–347 (2006). https://doi.org/10.1016/j.tics.2006.06.001. http://www.sciencedirect.com/science/article/pii/S1364661306001355

    Article  Google Scholar 

  15. Miles, M.B., Huberman, A.M., Saldaa, J.: Qualitative Data Analysis: A Methods Sourcebook, 3rd edn. Sage, Thousand Oaks (2014)

    Google Scholar 

  16. Ochoa, X., Worsley, M.: Editorial: augmenting learning analytics with multimodal sensory data. J. Learn. Anal. 3(2), 213–219 (2016). https://doi.org/10.18608/jla.2016.32.10. https://learning-analytics.info/journals/index.php/JLA/article/view/5081

    Article  Google Scholar 

  17. Oviatt, S., Cohen, A., Weibel, N.: Multimodal learning analytics: description of math data corpus for ICMI grand challenge workshop. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, ICMI 2013, Sydney, Australia, pp. 563–568. ACM, New York (2013). https://doi.org/10.1145/2522848.2533790. http://doi.acm.org/10.1145/2522848.2533790

  18. Schneider, B., Blikstein, P.: Unraveling students’ interaction around a tangible interface using multimodal learning analytics. J. Educ. Data Mining 7, 89–116 (2015)

    Google Scholar 

  19. Spikol, D., et al.: Exploring the interplay between human and machine annotated multimodal learning analytics in hands-on stem activities. In: Proceedings of the 6th International Learning Analytics & Knowledge Conference, vol. 6, pp. 522–523. Association for Computing Machinery (2016)

    Google Scholar 

  20. Taggart, R.W., Dressler, M., Kumar, P., Khan, S., Coppola, J.F.: Determining emotions via facial expression analysis software. In: Proceedings of Student-Faculty Research Day, CSIS, Pace University, May 2016

    Google Scholar 

  21. Wagner, J., Lingenfelser, F., Baur, T., Damian, I., Kistler, F., Andr, E.: The social signal interpretation (SSI) framework: multimodal signal processing and recognition in real-time. In: Proceedings of the 21st ACM International Conference on Multimedia, MM 2013, Barcelona, Spain, pp. 831–834. ACM, New York (2013). https://doi.org/10.1145/2502081.2502223. http://doi.acm.org/10.1145/2502081.2502223

  22. Wilensky, U.: Netlogo gaslab gas in a box model (1997). http://ccl.northwestern.edu/netlogo/models/GasLabGasinaBox

  23. Wilensky, U.: NetLogo (and NetLogo user manual) (1999). https://ccl.northwestern.edu/netlogo/

  24. Wilensky, U., Reisman, K.: Thinking like a wolf, a sheep, or a firefly: learning biology through constructing and testing computational theories–an embodied modeling approach. Cogn. Instr. 24(2), 171–209 (2006)

    Article  Google Scholar 

  25. Worsley, M., Blikstein, P.: A multimodal analysis of making. Int. J. Artif. Intell. Educ. 28(3), 385–419 (2018). https://doi.org/10.1007/s40593-017-0160-1

    Article  Google Scholar 

  26. Worsley, M., Scherer, S., Morency, L.P., Blikstein, P.: Exploring behavior representation for learning analytics. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, ICMI 2015, Seattle, Washington, USA, pp. 251–258. ACM, New York (2015). https://doi.org/10.1145/2818346.2820737. http://doi.acm.org/10.1145/2818346.2820737

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Authors and Affiliations

  1. Northwestern University, Evanston, IL, 60208, USA

    Kit Martin, Emily Q. Wang, Connor Bain & Marcelo Worsley

Authors
  1. Kit Martin
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  2. Emily Q. Wang
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  3. Connor Bain
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  4. Marcelo Worsley
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Corresponding author

Correspondence to Kit Martin .

Editor information

Editors and Affiliations

  1. University of Wisconsin–Madison, Madison, WI, USA

    Brendan Eagan

  2. University of Copenhagen, Copenhagen, Denmark

    Morten Misfeldt

  3. University of Wisconsin–Madison, Madison, WI, USA

    Amanda Siebert-Evenstone

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Martin, K., Wang, E.Q., Bain, C., Worsley, M. (2019). Computationally Augmented Ethnography: Emotion Tracking and Learning in Museum Games. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_12

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  • DOI: https://doi.org/10.1007/978-3-030-33232-7_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33231-0

  • Online ISBN: 978-3-030-33232-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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