Abstract
In working towards unraveling the mechanisms of productive collaborative learning, dual eye tracking is a potentially helpful methodology. Dual eye tracking is a method where eye-tracking data from people working on a task are analyzed jointly, for example to extract measures of joint visual attention. We explore how eye gaze relates to effective collaborative learning and how analysis of dual eye-tracking data might enhance analysis of other data streams. In this chapter, we identify three broad areas of analysis where dual eye tracking may enhance understanding of collaborative learning processes: (a) how eye gaze is associated with other communication measures, (b) how eye gaze is associated with features of the task environment, and (c) how eye gaze relates to learning outcomes. We present analyses in each of the three areas through joint visual attention, using a dataset of 28 fourth- and fifth-grade student dyads working on an intelligent tutoring system for fractions. By combining eye tracking, dialogue transcripts, tutor logs, and pre/post data, we show the potential of using dual eye tracking to better understand the collaborative learning process.
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References
Aleven, V., McLaren, B. M., Sewall, J., & Koedinger, K. R. (2009). A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105–154.
Aleven, V., McLaren, B. M., Sewall, J., van Velsen, M., Popescu, O., Demi, S., et al. (2016). Example-tracing tutors: Intelligent tutor development for non-programmers. International Journal of Artificial Intelligence in Education, 26(1), 224–269.
Belenky, D. M., Ringenberg, M., Olsen, J., Aleven, V., & Rummel, N. (2014). Using dual eye-tracking to evaluate students’ collaboration with an Intelligent Tutoring System for elementary-level fractions. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 176–181). Austin, TX: Cognitive Science Society.
Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1, 73–105.
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.
Griffin, Z. M., & Bock, K. (2000). What the eyes say about speaking. Psychological Science, 11(4), 274–279.
Janssen, J., & Bodemer, D. (2013). Coordinated computer-supported collaborative learning: Awareness and awareness tools. Educational Psychologist, 48(1), 40–55.
Jermann, P., Mullins, D., Nüssli, M. A., & Dillenbourg, P. (2011). Collaborative gaze footprints: Correlates of interaction quality. In H. Spada, G. Stahl, N. Miyake, & N. Law (Eds.), Connecting Computer-Supported Collaborative Learning to Policy and Practice: CSCL2011 Conference Proceedings (Vol. 1, No. EPFL-CONF-170043 pp. 184–191). Hong Kong, China: International Society of the Learning Sciences.
Jermann, P., & Nüssli, M. A. (2012). Effects of sharing text selections on gaze cross-recurrence and interaction quality in a pair programming task. In Association for Computing Machinery (Ed.), Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (pp. 1125–1134). New York: Association for Computing Machinery.
King, A. (1999). Discourse patterns for mediating peer learning. In A.M. O’Donnell & A. King (Eds.), Cognitive perspectives on peer learning (pp. 87–117). Mahwah, NJ: Lawrence Erlbaum Associates.
Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438, 237–329. doi:10.1016/j.physrep.2006.11.001.
Meyer, A. S., Sleiderink, A. M., & Levelt, W. J. M. (1998). Viewing and naming objects: Eye movements during noun phrase production. Cognition, 66, B25–B33.
Mullins, D., Rummel, N., & Spada, H. (2011). Are two heads always better than one? Differential effects of collaboration on students’ computer-supported learning in mathematics. International Journal of Computer-Supported Collaborative Learning, 6(3), 421–443.
Ohlsson, S. (1996). Learning from performance errors. Psychological Review, 103(2), 241–262.
Olsen, J., Belenky, D., Aleven, V., & Rummel, N. (2014). Using an intelligent tutoring system to support collaborative as well as individual learning. In S. Trausan-Matu, K. E. Boyer, M. Crosby, & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014 (pp. 134–143). Berlin: Springer.
Olsen, J. K., Rummel, N., & Aleven, V. (2015). Finding productive talk around errors in intelligent tutoring systems. In O. Lindwall, P. Häkkinen, T. Koschmann, P. Tchounikine, & S. Ludvigsen (Eds.), Exploring the Material Conditions of Learning: Proceedings of the International Conference on Computer Supported Collaborative Learning 2015 (Vol. 2, pp. 821–822). Gothenberg, Switzerland: International Society of the Learning Sciences.
Rau, M. A., Aleven, V., & Rummel, N. (2012). Sense making alone doesn’t do it: Fluency matters too! ITS support for robust learning with multiple representations. In S. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 174–184). Berlin/Heidelberg: Springer.
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, 1045–1060.
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.
Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007). Cognitive tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14(2), 249–255.
Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93(2), 346–362.
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.
Slavin, R. E. (1996). Research on cooperative learning and achievement: What we know, what we need to know. Contemporary Educational Psychology, 21(1), 43–69.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
Acknowledgments
We thank the CTAT team, Michael Ringenberg, Daniel Belenky, and Amos Glenn, for their help. This work was supported by Graduate Training Grant #R305B090023 and by Award #R305A120734, both from the U.S. Department of Education (IES).
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Olsen, J.K., Aleven, V., Rummel, N. (2017). Exploring Dual Eye Tracking as a Tool to Assess Collaboration. In: von Davier, A., Zhu, M., Kyllonen, P. (eds) Innovative Assessment of Collaboration. Methodology of Educational Measurement and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-33261-1_10
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DOI: https://doi.org/10.1007/978-3-319-33261-1_10
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