“I think you just got mixed up”: confident peer tutors hedge to support partners’ face needs

  • Michael Madaio
  • Justine Cassell
  • Amy Ogan


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.


Peer tutoring Hedging Rapport Self-efficacy Social bonds Indirectness 


  1. Ambady, N., & Rosenthal, R. (1992). Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. Psychological Bulletin, 111(2), 256 1992.CrossRefGoogle Scholar
  2. 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
  3. Brown, P., & Levinson, S. C. (1987). Politeness: Some universals in language use (Vol. 4). Cambridge: Cambridge University Press.Google Scholar
  4. 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.
  5. 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
  6. Cassell, J., & Bickmore, T. (2003). Negotiated collusion: Modeling social language and its relationship effects in intelligent agents. User Modeling and User-Adapted Interaction, 13(1), 89–132.CrossRefGoogle Scholar
  7. Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 6(3), 271–315.CrossRefGoogle Scholar
  8. Coates, J. (1987). Epistemic modality and spoken discourse. Transactions of the Philological Society, 85(1):110–131.Google Scholar
  9. Dame, N., & Tynan, R. (2005). The effects of threat sensitivity and face giving on dyadic psychological safety and upward communication. Journal of Applied Social Psychology, 35(2), 223–247.CrossRefGoogle Scholar
  10. 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
  11. Dillenbourg, P. (2015). Orchestration graphs: Modeling scalable education. Lausanne: EPFL Press.Google Scholar
  12. Feng, B., & Magen, E. (2015). Relationship closeness predicts unsolicited advice giving in supportive interactions. Journal of Social and Personal Relationships, 33(6), 751–767.CrossRefGoogle Scholar
  13. Fraser, B. (2010). Pragmatic competence: The case of hedging. New Approaches to Hedging, 9, 15–34.Google Scholar
  14. Gibson, S., & Dembo, M. H. (1984). Teacher efficacy: A construct validation. Journal of Educational Psychology, 76(4), 569.CrossRefGoogle Scholar
  15. Goffman, E. (2016). On face-work. Psychiatry, 18(3), 213–231.Google Scholar
  16. 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
  17. Kerssen-Griep, J., Trees, A. R., & Hess, J. A. (2008). Attentive Facework during instructional feedback: Key to perceiving mentorship and an optimal learning environment. Communication Education, 57(3), 312–332.CrossRefGoogle Scholar
  18. 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
  19. 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
  20. 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,
  21. 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
  22. Mojavezi, A., & Tamiz, M. P. (2012). The impact of teacher self-efficacy on the students’ motivation and achievement. Theory and Practice in Language Studies, 2(3), 483–491.CrossRefGoogle Scholar
  23. 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
  24. Mottet, T. P., & Beebe, S. A. (2002). Relationships between teacher nonverbal immediacy, student emotional response, and perceived student learning. Communication Research Reports, 19(1), 77–88.CrossRefGoogle Scholar
  25. 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
  26. 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
  27. 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.
  28. 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
  29. 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
  30. Palinscar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175.CrossRefGoogle Scholar
  31. Person, N. K., Kreuz, R. J., Zwaan, R. A., & Graesser, A. C. (1995). Pragmatics and pedagogy: Conversational rules and politeness strategies may inhibit effective tutoring. Cognition and Instruction, 13(2), 161–168.CrossRefGoogle Scholar
  32. 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
  33. 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
  34. Roscoe, R. D., & Chi, M. T. H. (2008). Tutor learning: The role of explaining and responding to questions. Instructional Science, 36(4), 321–350.CrossRefGoogle Scholar
  35. Rowan, B., Chiang, F., & Miller, R. (1997). Using research on Employees' performance to study the effects of teachers on Students' achievement. Sociology of Education, 70(4), 256–284.CrossRefGoogle Scholar
  36. 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
  37. Saklofske, D. H., Michayluk, J. O., & Randhawa, B. S. (1988). Teachers' efficacy and teaching behaviors. Psychological Reports, 63(2), 407–414.CrossRefGoogle Scholar
  38. Sinha, T. (2016). Cognitive Correlates of Rapport Dynamics in Longitudinal Peer Tutoring. Available at
  39. 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
  40. Spencer-Oatey, H. (2005). (Im)Politeness, face and perceptions of rapport: Unpackaging their bases and interrelationships. Politeness Research, 1(1), 95–119.Google Scholar
  41. Tegos, S., Demetriadis, S., Papadopoulos, P. M., & Weinberger, A. (2016). Conversational agents for academically productive talk: A comparison of directed and undirected agent interventions. International Journal of Computer-Supported Collaborative Learning, 11(4), 417–440.CrossRefGoogle Scholar
  42. Tickle-Degnen, L., & Rosenthal, R. (1990). The nature of rapport and its nonverbal correlates. Psychological Inquiry, 1(4), 285–293.CrossRefGoogle Scholar
  43. 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
  44. Walker, E., Rummel, N., & Koedinger, K. R. (2011). Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity. International Journal of Computer-Supported Collaborative Learning, 6(2), 279–306.CrossRefGoogle Scholar
  45. 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
  46. Weinberger, A., Ertl, B., Fischer, F., & Mandl, H. (2005). Epistemic and social scripts in computer–supported collaborative learning. Instructional Science, 33(1), 1–30.CrossRefGoogle Scholar
  47. Wichmann, A., & Rummel, N. (2013). Improving revision in wiki-based writing: Coordination pays off. Computers in Education, 62, 262–270.CrossRefGoogle Scholar
  48. 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
  49. Zhang, G. (2013). The impact of touchy topics on vague language use. Journal of Asian Pacific Communication, 23(1), 87–118.CrossRefGoogle Scholar
  50. 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
  51. 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

Copyright information

© International Society of the Learning Sciences, Inc. 2017

Authors and Affiliations

  1. 1.Human Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Language Technologies InstituteCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations