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Teachable agents are pedagogical agents that employ the ‘learning-by-teaching’ strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a ‘learning-by-teaching’ experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence. 

Teachable robots are a form of social robot for education, where learners engage in conversation to teach the robot like they would a peer. Part of the popularity of social robots is their ability to utilize social channels of communication to foster productive social experiences, interactions which help individuals grow and develop. Teachable robots have potential to utilize social channels of communication to create social experiences which can help learners develop self-efficacy, an individual’s belief in their ability to succeed. In this paper, we present a fully autonomous robot for middle school math; we iterate through three design phases and analyze responses to identify how to better foster productive social experiences for self efficacy. We report six design recommendations; for example, for low self-efficacy individuals, an ideal design should incorporate problem-solving statements and positivity to foster social experiences of mastery and social persuasion. 

Pedagogical agents have the potential to provide not only cognitive support to learners but socio-emotional support through social behavior. Socioemotional support can be a critical element to a learner’s success, influencing their self-efficacy and motivation. Several social behaviors have been explored with pedagogical agents including facial expressions, movement, and social dialogue; social dialogue has especially been shown to positively influence interactions. In this work, we explore the role of paraverbal social behavior or social behavior in the form of paraverbal cues such as tone of voice and intensity. To do this, we focus on the phenomenon of entrainment, where individuals adapt their paraverbal features of speech to one another. Paraverbal entrainment in human-human studies has been found to be correlated with rapport and learning. In a study with 72 middle school students, we evaluate the effects of entrainment with a teachable robot, a pedagogical agent that learners teach how to solve ratio problems. We explore how a teachable robot which entrains and introduces social dialogue influences rapport and learning; we compare with two baseline conditions: a social condition, in which the robot speaks socially, and a non-social condition, in which the robot neither entrains nor speaks socially. We find that a robot that does entrain and speaks socially results in significantly more learning.