We have carried out extensive analyses of three datasets to look at the role of rapport in learning. The first, collected by Erin Walker (Walker et al., 2011) contained data on peer tutoring over a chat interface by 130 high school students. We annotated the text data for the social functions of impoliteness and positivity, and the behaviors that might play a role in those social functions (such as criticisms, praise, insults, condescension, complaining, challenges, off-task behavior, etc.). Our analyses showed that negative behavior such as insults actually predicted learning gains (Ogan et al., 2012) and that both positivity and impoliteness could be automatically detected on the basis of the behaviors that make it up (Wang et al., 2012).
These results suggest that social functionality does play a role in peer tutoring, but that the nature of that social talk may not be the politeness and positivity that one might expect.
To follow up, we collected a second data set of face-to-face peer tutoring. We asked 12 dyads of high school students (half of the dyads were friends and half were strangers; half were girls and half were boys) to take turns tutoring one another in linear equations. The students came into the lab 5 times over 5 weeks. During each session both students in the dyad had the opportunity to tutor the other, with social time breaks built in between the tutoring (social time – tutoring – social time – tutoring – social time). Each session was videotaped from 3 angles so as to capture the face and torso of each individual, and a side view showing both participants. At the end of each session participants filled out a questionnaire about their rapport with and liking for the other, and at the beginning and end of the 5 weeks, the students took a test to evaluate their knowledge of linear equations.
Annotating Conversational Strategies
We have been transcribing and annotating the more than 90 hours (60 sessions) of human-human data. Based on prior literature in social psychology and communication studies, we have annotated non-verbal behaviors such as eye gaze, head nods, posture shifts and smiles, and verbal behavior such as insults, external vs. internal complaining, positive and negative self-disclosure, reference to shared experience, and more than 20 other phenomena. The longitudinal nature of the data, as well as the differences between friends and strangers has allowed us to see how friends vs. strangers weather frustration, how they manage a task where one partner (the tutor) is given more power than the other, and what kinds of social support strategies enhance learning and what kinds diminish it.
This dataset has also allowed us to automatically detect friends vs. strangers based on their acoustic and nonverbal behavior (Zhou et al., 2013).
Finally, we used a think-aloud protocol to collect data about students tutoring a virtual agent (called a “teachable agent”) to see whether the results we obtained for human-human tutoring translated to a context where one member of the dyad was a computer. Here too, to our surprise, we found that students who insulted the agent, and students who engaged with the agent and referred to it as “you” were more likely to learn than students who were polite, or students who referred to the agent as “she” or “it”(Ogan et al,. 2012).