Real-time mutual gaze perception enhances collaborative learning and collaboration quality

  • Bertrand SchneiderEmail author
  • Roy Pea


In this paper we present the results of an eye-tracking study on collaborative problem-solving dyads. Dyads remotely collaborated to learn from contrasting cases involving basic concepts about how the human brain processes visual information. In one condition, dyads saw the eye gazes of their partner on the screen; in a control group, they did not have access to this information. Results indicated that this real-time mutual gaze perception intervention helped students achieve a higher quality of collaboration and a higher learning gain. Implications for supporting group collaboration are discussed.


Collaborative learning Awareness tool Eye-tracking 



We gratefully acknowledge grant support from the National Science Foundation (NSF) for this work from the LIFE Center (NSF #0835854).


  1. Alavi, H. S., & Dillenbourg, P. (2012). An ambient awareness tool for supporting supervised collaborative problem solving. IEEE Transactions on Learning Technologies, 5(3), 264–274.CrossRefGoogle Scholar
  2. Aronson, E., Blaney, N., Sikes, J., Stephan, G., & Snapp, M. (1978). The jigsaw classroom. Beverly Hills: Sage Publication.Google Scholar
  3. 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.CrossRefGoogle Scholar
  4. Bakeman, R., & Adamson, L. B. (1984). Coordinating attention to people and objects in mother-infant and peer-infant interaction. Child Development, 55(4), 1278–1289.CrossRefGoogle Scholar
  5. Baldwin, D. A. (1991). Infants’ contribution to the achievement of joint reference. Child Development,62(5), 874–890.Google Scholar
  6. Barron, B. (2003). When smart groups fail. The Journal of the Learning Sciences, 12(3), 307–359.CrossRefGoogle Scholar
  7. Barron, B., & Roschelle, J. (2009). Shared cognition. In E. Anderman (Ed.), Psychology of classroom learning: An encyclopedia (pp. 819–823). Detroit: Macmillan Reference USA.Google Scholar
  8. Barron, B., Pea, R. D., & Engle, R. (2013). Advancing understanding of collaborative learning with data derived from video records. In C. Hmelo-Silver, A. O’Donnell, C. Chinn, & C. Chan (Eds.), International handbook of collaborative learning (pp. 203–219). New York: Taylor & Francis.Google Scholar
  9. Bates, E., Thal, D., Whitesell, K., Fenson, L., & Oakes, L. (1989). Integrating language and gesture in infancy. Developmental Psychology, 25(6), 1004–1019.CrossRefGoogle Scholar
  10. Beatty, J. (1982). Task evoked pupillary responses, processing load and structure of processing resources. Psychological Bulletin, 91(2), 276–292.CrossRefGoogle Scholar
  11. Beatty, J., & Lucero-Wagoner, B. (2000). Pupillary system. In J. T. Cacioppo, L. G. Tassinary, & G. Berntson (Eds.), Handbook of psychophysiology. New York: Cambridge University Press.Google Scholar
  12. Biederman, I., & Shiffrar, M. M. (1987). Sexing day-old chicks: A case study and expert systems analysis of a difficult perceptual-learning task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(4), 640–645.Google Scholar
  13. Boersma, P. (2002). Praat, a system for doing phonetics by computer. Glot International, 5(9/10), 341–345.Google Scholar
  14. Brennan, S. E., Chen, X., Dickinson, C. A., Neider, M. B., & Zelinsky, G. J. (2008). Coordinating cognition: The costs and benefits of shared gaze during collaborative search. Cognition, 106(3), 1465–1477.CrossRefGoogle Scholar
  15. Brooks, R., & Meltzoff, A. N. (2008). Infant gaze following and pointing predict accelerated vocabulary growth through two years of age: A longitudinal, growth curve modeling study. Journal of Child Language, 35(1), 207–220.CrossRefGoogle Scholar
  16. Charman, T., Baron-Cohen, S., Swettenham, J., Baird, G., Cox, A., & Drew, A. (2000). Testing joint attention, imitation, and play as infancy precursors to language and theory of mind. Cognitive Development, 15(4), 481–498.CrossRefGoogle Scholar
  17. Cherubini, M., Nüssli, M., and Dillenbourg, P. (2008). Deixis and gaze in collaborative work at a distance (over a shared map): a computational model to detect misunderstandings. In Proceedings of the 2008 Symposium on Eye Tracking Research & Applications (Savannah, Georgia, March 26–28, 2008). ETRA ‘08. (pp. 173–180). New York, NY: ACM.Google Scholar
  18. Clark, H. H. (1996). Using language. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  19. Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  20. Cress, U. (2008). The need for considering multilevel analysis in CSCL research—An appeal for the use of more advanced statistical methods. International Journal of Computer-Supported Collaborative Learning, 3(1), 69–84.CrossRefGoogle Scholar
  21. Dillenbourg, P., Baker, M., Blaye, A., & O’Malley, C. (1996). The evolution of research on collaborative learning. In P. Reimann & H. Spada (Eds.), Learning in humans and machine: Towards an interdisciplinary learning science (pp. 189–211). Oxford: Elsevier.Google Scholar
  22. Dodge, R., & Cline, T. S. (1901). The angle velocity of eye movements. Psychological Review, 8(2), 145–157.CrossRefGoogle Scholar
  23. Duchowski, A. T. (2007). Eye Tracking Methodology: Theory and Practice. SpringerGoogle Scholar
  24. Hanna, J. E., & Brennan, S. E. (2007). Speakers’ eye gaze disambiguates referring expressions early during face-to-face conversation. Journal of Memory and Language, 57(4), 596–615.CrossRefGoogle Scholar
  25. Hayes, A. F., & Krippendorff, K. (2007). Answering the call for a standard reliability measure for coding data. Communication Methods and Measures, 1, 77–89.Google Scholar
  26. Jacob, R. J., & Karn, K. S. (2003). Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. Mind, 2(3), 4.Google Scholar
  27. Jermann, P., Mullins, D., Nuessli, M.-A., & Dillenbourg, P. (2001). Collaborative gaze footprints: correlates of interaction quality. In Spada, H., Stahl, G., Miyake, N., & Law, N. (Eds.), Connecting Computer-Supported Collaborative Learning to Policy and Practice: CSCL2011 Conference Proceedings, Hong Kong, July 4–8, 2011, Volume I - Long Papers, (pp. 184–191).Google Scholar
  28. Kendon, A. (1990). Conducting interaction: Patterns of behavior in focused encounters. Cambridge: Cambridge University Press.Google Scholar
  29. Kim, T., & Pentland, A. (2009, March). Understanding effects of feedback on group collaboration. In Proc. of the AAAI Spring Symposium on Human Behavior Modeling (pp. 1–6).Google Scholar
  30. Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: A review of the research. Computers in Human Behavior, 19(3), 335–353.CrossRefGoogle Scholar
  31. Leudar, I., Costall, A., & Francis, D. (2004). Theory of mind: A critical assessment. Theory & Psychology, 14(5), 571–578.CrossRefGoogle Scholar
  32. Lindgren, R., & Pea, R. (2012). Inter-identity technologies for learning. Proceedings of the International Conference of the Learning Sciences (pp. 427–434). Sydney: Australia.Google Scholar
  33. Liu, Y., Hsueh, P.Y., Lai, J., Sangin, M., Nussli, M.-A., Dillenbourg, P. (2009, June). Who is the expert? Analyzing gaze data to predict expertise level in collaborative applications. Proc. of IEEE Int. Conference on Multimedia and Expo: ICME 2009 (898–901). Google Scholar
  34. 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.CrossRefGoogle Scholar
  35. Mundy, P., Sigman, M., & Kasari, C. (1990). A longitudinal study of joint attention and language development in autistic children. Journal of Autism and Developmental Disorders, 20(1), 115–128.CrossRefGoogle Scholar
  36. Nüssli, M. A., Jermann, P., Sangin, M., & Dillenbourg, P. (2009). Collaboration and abstract representations: Towards predictive models based on raw speech and eye-tracking data. In Proceedings of the 9th International Conference on Computer Supported Collaborative Learning (pp. 78–82).Google Scholar
  37. Pea, R. D. (1987). Socializing the knowledge transfer problem. International Journal of Educational Research, 11(6), 639–663.CrossRefGoogle Scholar
  38. Piaget, J. (1998). The language and thought of the child. Routledge: Psychology Press.Google Scholar
  39. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879.CrossRefGoogle Scholar
  40. Richardson, D. C., & Dale, R. (2005). Looking to understand: The coupling between speakers’ and listeners’ eye movements and its relationship to discourse comprehension. Cognitive Science, 29(6), 1045–1060.CrossRefGoogle Scholar
  41. Richardson, D. C., Dale, R., & Kirkham, N. Z. (2007). The art of conversation is coordination: Common ground and the coupling of eye movements during dialogue. Psychological Science, 18(5), 407–413.CrossRefGoogle Scholar
  42. Roth, W. M. (2001). Gestures: Their role in teaching and learning. Review of Educational Research, 71(3), 365–392.CrossRefGoogle Scholar
  43. Rothman, K. J. (1990). No adjustments are needed for multiple comparisons. Epidemiology, 1(1), 43–46.CrossRefGoogle Scholar
  44. Salomon, G., & Globerson, T. (1989). When teams do not function the way they ought to. International Journal of Educational research, 13(1), 89–100.CrossRefGoogle Scholar
  45. Sangin, M. (2009). Peer knowledge modeling in computer supported collaborative learning. (Doctoral Dissertation). Retrieved from Last access: 03/08/2013.
  46. Schwartz, D. L. (1995). The emergence of abstract representations in dyad problem solving. Journal of the Learning Sciences, 4(3), 321–354.CrossRefGoogle Scholar
  47. Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition & Instruction, 16(4), 475–522.CrossRefGoogle Scholar
  48. Schwartz, D., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129–184.CrossRefGoogle Scholar
  49. Shaer, O., Strait, M., Valdes, C., Feng, T., Lintz, M., & Wang, H. (2011, May). Enhancing genomic learning through tabletop interaction. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2817–2826). New York: ACM.Google Scholar
  50. Sheingold, K., Hawkins, J., & Char, C. (1984). “I’m the thinkist, you’re the typist”: The interaction of technology and the social life of classrooms. Journal of Social Issues, 40(3), 49–61.CrossRefGoogle Scholar
  51. Stem, D. (1977). The first relationship. Cambridge: Harvard University Press.Google Scholar
  52. Tomasello, M. (1995). Joint attention as social cognition. In C. Moore & P. J. Dunham (Eds.), Joint attention: Its origins and role in development (pp. 103–130). Hillsdale: Erlbaum Associates, Inc.Google Scholar
  53. Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. New York: John Wiley & Sons.Google Scholar
  54. Vygotski, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge: Harvard University Press.Google Scholar

Copyright information

© International Society of the Learning Sciences, Inc. and Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Graduate School of EducationStanford UniversityStanfordUSA

Personalised recommendations