Skip to main content
SpringerLink
Log in

Unpacking the relationship between existing and new measures of physiological synchrony and collaborative learning: a mixed methods study

  • Published:
International Journal of Computer-Supported Collaborative Learning Aims and scope Submit manuscript

Abstract

Over the last decade, there has been a renewed interest in capturing twenty-first century skills using new data collection tools. In this article, we leverage an existing dataset where electrodermal activity (EDA) was used to identify markers of productive collaboration. The data came from 42 pairs of participants (N = 84) who had no coding experience and were asked to program a robot to solve a variety of mazes. Because little is known on how physiological synchrony relates to collaborative learning, we explored four different measures of synchrony: Signal Matching (SM), Instantaneous Derivative Matching (IDM), Directional Agreement (DA) and Pearson’s Correlation (PC). Overall, we found PC to be positively associated with learning gains (r = 0.35) and DA with collaboration quality (r = 0.3). To gain further insights into these results, we also qualitatively analyzed two groups and identified situations with high or low physiological synchrony. We observed higher synchrony values when members of a productive group reacted to an external event (e.g., following instructions, receiving a hint), oscillations when they were watching a video or interacting with each other, and lower values when they were programming and / or seem to be confused. Based on these results, we developed a new measure of collaboration using electrodermal data: we computed the number of cycles between low and high synchronization. We found this measure to be significantly correlated with collaboration quality (r = 0.57) and learning gains (r = 0.47). This measure was not significantly correlated with the measures of physiological synchrony mentioned above, suggesting that it is capturing a different construct. We compare those results with prior studies and discuss implications for measuring collaborative process through physiological sensors.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abrahamson, D., Trninic, D., Gutiérrez, J. F., Huth, J., & Lee, R. G. (n.d.). Hooks and shifts: A dialectical study of mediated discovery (pp. 1–31). Knowledge and Learning: Technology.

  • Arroyo, I., Cooper, D. G., Burleson, W., Woolf, B. P., Muldner, K., & Christopherson, R. (2009). Emotion Sensors Go To School., 200, 17–24.

    Google Scholar 

  • Bachour, K., Kaplan, F., & Dillenbourg, P. (2010). An interactive table for supporting participation balance in face-to-face collaborative learning. IEEE Transactions on Learning Technologies, 3(3), 203–213. https://doi.org/10.1109/TLT.2010.18.

    Article  Google Scholar 

  • Baker, M. (2003). Computer-mediated argumentative interactions for the co-elaboration of scientific notions. In J. Andriessen, M. Baker, & D. Suthers (Eds.), Arguing to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning Environments (pp. 47–78). Springer Netherlands. https://doi.org/10.1007/978-94-017-0781-7_3.

  • Baker, M., Hansen, T., Joiner, R., & Traum, D. (1999). The role of grounding in collaborative learning tasks. Collaborative Learning: Cognitive and Computational Approaches, 31, 63.

    Google Scholar 

  • Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220–238.

    Article  Google Scholar 

  • Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. 1–25.

  • Buder, J. (2011). Group awareness tools for learning: Current and future directions. Computers in Human Behavior, 27(3), 1114–1117.

    Article  Google Scholar 

  • Chanel, G., Kivikangas, J. M., & Ravaja, N. (2012). Physiological compliance for social gaming analysis: Cooperative versus competitive play. Interacting with Computers, 24(4), 306–316.

    Article  Google Scholar 

  • Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception–behavior link and social interaction. Journal of Personality and Social Psychology, 76(6), 893–910. https://doi.org/10.1037/0022-3514.76.6.893.

    Article  Google Scholar 

  • Clark, H. H., & Wilkes-Gibbs, D. (1986). Referring as a collaborative process. Cognition, 22(1), 1–39. https://doi.org/10.1016/0010-0277(86)90010-7.

    Article  Google Scholar 

  • Cooper, M. A. (1999). Classroom choices from a cognitive perspective on peer learning. In Cognitive perspectives on peer learning (pp. 215–233). Lawrence Erlbaum Associates Publishers.

  • Damon, W. (1984). Peer education: The untapped potential. Journal of Applied Developmental Psychology, 5(4), 331–343. https://doi.org/10.1016/0193-3973(84)90006-6.

    Article  Google Scholar 

  • Damon, W., & Phelps, E. (1989). Critical distinctions among three approaches to peer education. International Journal of Educational Research, 13(1), 9–19. https://doi.org/10.1016/0883-0355(89)90013-X.

    Article  Google Scholar 

  • Dawson, M. E., Schell, A. M., & Filion, D. L. (2007). The electrodermal system. Handbook of Psychophysiology, 2, 200–223.

    Google Scholar 

  • Dede, C. (2010). Comparing frameworks for 21st century skills. 21st Century Skills: Rethinking How Students Learn, 20, 51–76.

    Google Scholar 

  • Elkins, A. N., Muth, E. R., Hoover, A. W., Walker, A. D., Carpenter, T. L., & Switzer, F. S. (2009). Physiological compliance and team performance. Applied Ergonomics, 40(6), 997–1003.

    Article  Google Scholar 

  • Garbarino, M., Lai, M., Bender, D., Picard, R. W., & Tognetti, S. (2014). Empatica E3—A wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition. 39–42.

  • Haataja, E., Malmberg, J., & Järvelä, S. (2018). Monitoring in collaborative learning: Co-occurrence of observed behavior and physiological synchrony explored. Computers in Human Behavior, 87, 337–347. https://doi.org/10.1016/j.chb.2018.06.007.

    Article  Google Scholar 

  • Hadwin, A. F., Järvelä, S., & Miller, M. (2011) Self-regulated, co-regulated, and socially shared regulation of learning. In Handbook of self-regulation of learning and performance (pp. 65–84). Routledge/Taylor & Francis Group.

  • Henning, R. A., Boucsein, W., & Gil, M. C. (2001). Social–physiological compliance as a determinant of team performance. International Journal of Psychophysiology, 40(3), 221–232.

    Article  Google Scholar 

  • Henning, R., Armstead, A., & Ferris, J. (2009). Social psychophysiological compliance in a four-person research team. Applied Ergonomics, 40(6), 1004–1010.

    Article  Google Scholar 

  • Järvelä, S., Kivikangas, J. M., Kätsyri, J., & Ravaja, N. (2014). Physiological linkage of dyadic gaming experience. Simulation & Gaming, 45(1), 24–40.

    Article  Google Scholar 

  • Martinez Maldonado, R., Kay, J., Yacef, K., & Schwendimann, B. (2012). An interactive Teacher’s dashboard for monitoring groups in a multi-tabletop learning environment. In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Intelligent tutoring systems (pp. 482–492). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  • Meier, A., Spada, H., & Rummel, N. (2007). A rating scheme for assessing the quality of computer-supported collaboration processes. International Journal of Computer-Supported Collaborative Learning, 2(1), 63–86. https://doi.org/10.1007/s11412-006-9005-x.

    Article  Google Scholar 

  • Miyake, N. (1986). Constructive interaction and the iterative process of understanding. Cognitive Science, 10(2), 151–177.

  • Montague, E., Xu, J., & Chiou, E. (2014). Shared experiences of technology and trust: An experimental study of physiological compliance between active and passive users in technology-mediated collaborative encounters. IEEE Transactions on Human-Machine Systems, 44(5), 614–624.

    Article  Google Scholar 

  • Parkinson, B., & Simons, G. (2009). Affecting others: Social appraisal and emotion contagion in everyday decision making. Personality and Social Psychology Bulletin, 35(8), 1071–1084. https://doi.org/10.1177/0146167209336611.

    Article  Google Scholar 

  • Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016). Investigating collaborative learning success with physiological coupling indices based on electrodermal activity. 64–73.

  • Roschelle, J. (1992). Learning by collaborating: Convergent conceptual change. The Journal of the Learning Sciences, 2(3), 235–276.

    Article  Google Scholar 

  • Salomon, G., & Globerson, T. (1989). When teams do not function the way they ought to. International Journal of Educational Research, 13(1), 89–99. https://doi.org/10.1016/0883-0355(89)90018-9.

  • Schneider, B. (2019). Unpacking collaborative learning processes during hands-on activities using Mobile eye-tracking. In Proceedings of 13th International Conference of Computer Supported Collaborative Learning (pp. 156–165).

    Google Scholar 

  • Schneider, B., & Pea, R. (2013). Real-time mutual gaze perception enhances collaborative learning and collaboration quality. International Journal of Computer-Supported Collaborative Learning, 8(4), 375–397. https://doi.org/10.1007/s11412-013-9181-4.

    Article  Google Scholar 

  • Stahl, G. (2007). Meaning making in CSCL: Conditions and preconditions for cognitive processes by groups. Proceedings of the 8th Iternational Conference on Computer Supported Collaborative Learning, 652–661.

  • Starr, E., Dich, Y., Reilly, J., & Schneider, B. (2018). Using multi-modal learning analytics to support and measure collaboration in co-located dyads. In 12th international conference of the learning sciences. London: UK.

    Google Scholar 

  • Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. (2015). Automatic identification of artifacts in electrodermal activity data. 1934–1937.

  • Tschan, F. (2002). Ideal cycles of communication (or cognitions) in triads, dyads, and individuals. Small Group Research, 33(6), 615–643.

  • Wang, H., Chignell, M., & Ishizuka, M. (2006). Empathic tutoring software agents using real-time eye tracking. Proceedings of the 2006 Symposium on Eye Tracking Research & Applications, 73–78. https://doi.org/10.1145/1117309.1117346.

  • Webb, N. M., & Palincsar, A. S. (1996). Group processes in the classroom. Prentice Hall International.

    Google Scholar 

  • Webb, N. M., Troper, J. D., & Fall, R. (1995). Constructive activity and learning in collaborative small groups. Journal of Educational Psychology, 87(3), 406–423. https://doi.org/10.1037/0022-0663.87.3.406.

    Article  Google Scholar 

  • Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71–95. https://doi.org/10.1016/j.compedu.2005.04.003.

    Article  Google Scholar 

  • Weintrop, D., & Wilensky, U. (2015). Using commutative assessments to compare conceptual understanding in blocks-based and text-based programs. 11th Annual ACM Conference on International Computing Education Research, ICER 2015.

Download references

Acknowledgements

This work was funded by the Harvard Graduate School of Education (HGSE) through the Dean Venture Funds. We also thank the Harvard Decision Science Lab (HDSL) for their support throughout this project.

Author information

Authors and Affiliations

  1. Graduate School of Education, Harvard University, Cambridge, MA, USA

    Bertrand Schneider, Yong Dich & Iulian Radu

Authors
  1. Bertrand Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Dich
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iulian Radu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bertrand Schneider.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schneider, B., Dich, Y. & Radu, I. Unpacking the relationship between existing and new measures of physiological synchrony and collaborative learning: a mixed methods study. Intern. J. Comput.-Support. Collab. Learn 15, 89–113 (2020). https://doi.org/10.1007/s11412-020-09318-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11412-020-09318-2

Keywords

Search

Navigation