“I think you just got mixed up”: confident peer tutors hedge to support partners’ face needs
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During collaborative learning, computer-supported or otherwise, students balance task-oriented goals with the interpersonal goals of relationship-building. This means that in peer tutoring, some pedagogically beneficial behaviors may be avoided by peer tutors due to their likelihood to get in the way of relationship-building. In this paper, we explore how the interpersonal closeness between students in a peer tutoring dyad and the peer tutors’ instructional self-efficacy impacts those tutors’ delivery style of various tutoring moves, and explore the impact those tutoring move delivery styles have on their partners’ learning. We found that tutors with lower social closeness with their tutees provide more positive feedback to their tutee and use more indirect instructions and comprehension-monitoring, but this is only the case for tutors with greater tutoring self-efficacy. And in fact, those tutees solved more problems and learned more when their tutors hedged instructions and comprehension-monitoring, respectively. We found no effect of hedging for dyads with greater social closeness, on the other hand, suggesting that interpersonal closeness may reduce the face-threat of direct instructions and comprehension-monitoring, and hence reduce the need for indirectness, while tutors’ instructional self-efficacy allows tutors to use those moves without feeling threatened themselves. These results emphasize that designers of CSCL systems should understand the nature of how the interpersonal closeness between collaborating students intersects with those students’ self-efficacy to impact the use and delivery of their learning behaviors, in order to best support them in collaborating effectively.
KeywordsPeer tutoring Hedging Rapport Self-efficacy Social bonds Indirectness
- Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B: Methodological, 57(1), 289–300.Google Scholar
- Brown, P., & Levinson, S. C. (1987). Politeness: Some universals in language use (Vol. 4). Cambridge: Cambridge University Press.Google Scholar
- Boyer, K. E., Phillips, R., Ha, E. Y., Wallis, M. D., Vouk, M. A., & Lester, J. C. (2009). Modeling dialogue structure with adjacency pair analysis and hidden Markov models. Proceedings of NAACL HLT (Short Papers), 19–26. https://doi.org/10.3115/1620853.1620869.
- Carmien, S., Kollar, I., Fischer, G., & Fischer, F. (2007). The interplay of internal and external scripts. Scripting Computer-Supported Collaborative Learning, 6, 303–326.Google Scholar
- Coates, J. (1987). Epistemic modality and spoken discourse. Transactions of the Philological Society, 85(1):110–131.Google Scholar
- Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. P. A. Kirschner. Three worlds of CSCL. Can we support CSCL?, Heerlen, Open Universiteit Nederland, pp.61-91.Google Scholar
- Dillenbourg, P. (2015). Orchestration graphs: Modeling scalable education. Lausanne: EPFL Press.Google Scholar
- Fraser, B. (2010). Pragmatic competence: The case of hedging. New Approaches to Hedging, 9, 15–34.Google Scholar
- Goffman, E. (2016). On face-work. Psychiatry, 18(3), 213–231.Google Scholar
- Johnson, W. L., & Rizzo, P. (2004). Politeness in tutoring dialogs: Run the factory, that’s what I’d do. In Seventh International Conference on Intelligent Tutoring Systems (pp. 67–76). Maceió.Google Scholar
- Kruger, J., Endriss, U., Fernández, R., & Qing, C. (2014). Axiomatic analysis of aggregation methods for collective annotation. In Proceedings of the 2014 international Conference on Autonomous Agents and Multi-Agent Systems (pp. 1185-1192). International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
- Madaio, M. A., Ogan, A., & Cassell, J. (2016). The effect of friendship and tutoring roles on reciprocal peer tutoring strategies. In International Conference on Intelligent Tutoring Systems (pp. 423-429). Springer, Cham.Google Scholar
- Madaio, M., Ogan, A., & Cassell, J. (2017). Using Temporal Association Rule Mining to Predict Dyadic Rapport in Peer Tutoring. In Proceedings of the 10th International Conference on Educational Data Mining, http://educationaldatamining.org/EDM2017/proc_files/papers/paper_118.pdf.
- Matsuyama, Y., Bhardwaj, A., Zhao, R., Romero, O.J., Akoju, S., & Cassell, J. (2016). Socially-aware animated intelligent personal assistant agent. In 17th Annual SIGdial Meeting on Discourse and Dialogue. Pittsburgh: Carnegie Mellon University.Google Scholar
- Morand, D. A., & Ocker, R. J. (2003). Politeness theory and computer-mediated communication: A sociolinguistic approach to analyzing relational messages. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on (pp. 10-pp). IEEE.Google Scholar
- Neary-Sundquist, C. (2013). The use of hedges in the speech of ESL learners. Elia: Estudios de Lingüística Inglesa Aplicada, 13, 149–174.Google Scholar
- Ogan, A., Finkelstein, S., Walker, E., Carlson, R., & Cassell, J. (2012). Rudeness and rapport: Insults and learning gains in peer tutoring. In Proceedings of the 11th International Conference on Intelligent Tutoring Systems (pp. 11–21). Berlin: Springer.Google Scholar
- Ogan, A., Walker, E., Baker, R., Rodrigo, M. M. T., Soriano, J. C., & Castro, M. J. (2015). Towards understanding how to assess help-seeking behavior across cultures. International Journal of Artificial Intelligence in Education, 25(2), 229–248. https://doi.org/10.1007/s40593-014-0034-8.
- Ohlsson, S., Di Eugenio, B., Chow, B., Fossati, D., Lu, X., & Kershaw, T.C. (2007). Beyond the code-and-count analysis of tutoring dialogues. In Proceedings of the 2007 Conference on AI in Education, 158, p. 349.Google Scholar
- Olsen, J. K., Belenky, D. M., Aleven, V., & Rummel, N. (2014). Using an intelligent tutoring system to support collaborative as well as individual learning. In International Conference on Intelligent Tutoring Systems (pp. 134-143). Springer, Cham.Google Scholar
- Prince, E., Frader, J. & Bosk, C. (1982). On hedging in physician-physician discourse. In R. J. Di Pietro (Ed.), Linguistics and the professions. Proceedings of the second annual delaware symposium on language studies (pp. 83–97). Norwood: Ablex.Google Scholar
- Ray, D. G., Neugebauer, J., Sassenberg, K., Buder, J., & Hesse, F. W. (2013). Motivated shortcomings in explanation: The role of comparative self-evaluation and awareness of explanation recipient's knowledge. Journal of Experimental Psychology: General, 142(2), 445.Google Scholar
- Rowland, T. (2007). ‘Well maybe not exactly, but it’s around fifty basically?’: Vague language in mathematics classrooms. In J. Cutting (ed.), Vague language explored (pp. 79–96). Hampshire: Palgrave MacmillanGoogle Scholar
- Sinha, T. (2016). Cognitive Correlates of Rapport Dynamics in Longitudinal Peer Tutoring. Available at http://tinyurl.com/RapportDynamicsSinha2016.
- Sinha, T., & Cassell, J. (2015). 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). ACM.Google Scholar
- Spencer-Oatey, H. (2005). (Im)Politeness, face and perceptions of rapport: Unpackaging their bases and interrelationships. Politeness Research, 1(1), 95–119.Google Scholar
- Tracy, K., & Coupland, N. (1990). Multiple goals in discourse: An overview of issues. Journal of Language and Social Psychology, 9(1–2), 1–13.Google Scholar
- Wang, X., Wen, M., & Rose, C. (2017). Contrasting Explicit and Implicit Support for Transactive Exchange in Team Oriented Project Based Learning In Smith, B. K., Borge, M., Mercier, E., and Lim, K. Y. (Eds.). (2017). Making a difference: Prioritizing equity and access in CSCL, 12th International conference on computer supported collaborative learning (CSCL) 2017, Volume 1. Philadelphia: International Society of the Learning Sciences.Google Scholar
- Yu, Z., Gerritsen, D., Ogan, A., Black, A., & Cassell, J. (2013). Automatic prediction of friendship via multi-modeldyadic features. In 14th Annual SIGdial Meeting on Discourse and Dialogue. Metz: Association for Computational Linguistics.Google Scholar
- Zhao, R., Papangelis, A., & Cassell, J. (2014). Towards a dyadic computational model of rapport management for human-virtual agent interaction. In International Conference on Intelligent Virtual Agents (pp. 514-527). Springer, Cham.Google Scholar
- Zhao, R., Sinha, T., Black, A. W., & Cassell, J. (2016). Socially-aware virtual agents: Automatically assessing dyadic rapport from temporal patterns of behavior. In International Conference on Intelligent Virtual Agents (pp. 218-233). Springer International Publishing.Google Scholar