Abstract
Teaching others has been shown to be an activity in which students can learn new information in both human-human (peer-tutoring) and human-computer interactions (teachable robots). One factor that may help foster learning and engagement when teaching others is the development of positive rapport and perceptions between the tutor, tutee, and robot. However, it is not clear what factors might affect the development of rapport. We explore whether having two students work together with a teachable robot might facilitate positive perceptions of the robot, rapport-building, and positive learning outcomes. In an exploratory pilot study, students were assigned to either work together in dyads (n = 28) or individually (n = 12) to help a teachable robot (Emma) solve math problems. Preliminary results showed that those who worked in a dyad had generally more positive perceptions of the robot than those who worked individually. These benefits were not observed for rapport where there were few differences between dyads and individuals, or learning where there was no difference on the posttest. We discuss the implications of these results for future research to explore the potential benefits of collaborative teaching of a robot learner.
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References
Reeves, T.C., Oh, E.G.: The goals and methods of educational technology research over a quarter century (1989–2014). Educ. Technol. Res. Dev. 65(2), 325–339 (2016). https://doi.org/10.1007/s11423-016-9474-1
Walker, E., Rummel, N., Koedinger, K.R.: Adaptive intelligent support to improve peer tutoring in algebra. Int. J. Artif. Intell. Educ. 24(1), 33–61 (2014)
Lubold, N., Walker, E., Pon-Barry, H., Flores, Y., Ogan, A.: Using iterative design to create efficacy-building social experiences with a teachable robot. International Society of the Learning Sciences, Inc. [ISLS] (2018)
Ogan, A., Finkelstein, S., Walker, E., Carlson, R., Cassell, J.: Rudeness and rapport: insults and learning gains in peer tutoring. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 11–21. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30950-2_2
El Hamamsy, L., Johal, W., Asselborn, T., Nasir, J., Dillenbourg, P.: Learning by collaborative teaching: an engaging multi-party cowriter activity. In: 28th IEEE International Conference on Robot and Human Interactive Communication, pp. 1–8 (2019)
Bartneck, C., Kulić, D., Croft, E., Zoghbi, S.: Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int. J. Soc. Robot. 1(1), 71–81 (2009)
Sinha, T., Cassell, J.: We click, we align, we learn: impact of influence and convergence processes on student learning and rapport building. In: Proceedings of the 1st Workshop on Modeling Interpersonal Synchrony and Influence, pp. 13–20 (2015)
Acknowledgements
This work was supported by Grant No. 2024645 from the National Science Foundation, Grant No. 220020483 from the James S. McDonnell Foundation, and a University of Pittsburgh Learning Research and Development Center internal award.
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Steele, C. et al. (2022). It Takes Two: Examining the Effects of Collaborative Teaching of a Robot Learner. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_125
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