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.
more »
« less
Dyadic Stance in Natural Language Communication with a Teachable Robot
Learning companion robots can provide personalized learning interactions to engage students in many domains including STEM. For successful interactions, students must feel comfortable and engaged. We describe an experiment with a learning companion robot acting as a teachable robot; based on human-to-human peer tutoring, students teach the robot how to solve math problems. We compare student attitudes of comfort, attention, engagement, motivation, and physical proximity for two dyadic stance formations: a face-to-face stance and a side-by-side stance. In human-robot interaction experiments, it is common for dyads to assume a face-to-face stance, while in human-to-human peer tutoring, it is common for dyads to sit in side-by-side as well as face-to-face formations. We find that students in the face-to-face stance report stronger feelings of comfort and attention, compared to students in the side-by-side stance. We find no difference between stances for feelings of engagement, motivation, and physical proximity.
more »
« less
- Award ID(s):
- 1637947
- NSF-PAR ID:
- 10076256
- Date Published:
- Journal Name:
- HRI '18 Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
- Page Range / eLocation ID:
- 85 - 86
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
In peer tutoring, the learner is taught by a colleague rather than by a traditional tutor. This strategy has been shown to be effective in human tutoring, where students have higher learning gains when taught by a peer instead of a traditional tutor. Similar results have been shown in child-robot interactions studies, where a peer robot was more effective than a tutor robot at teaching children. In this work, we compare skill increase and perception of a peer robot to a tutor robot when teaching adults. We designed a system in which a robot provides personalized help to adults in electronic circuit construction. We compare the number of learned skills and preferences of a peer robot to a tutor robot. Participants in both conditions improved their circuit skills after interacting with the robot. There were no significant differences in number of skills learned between conditions. However, participants with low prior domain knowledge learned significantly more with a peer robot than a tutor robot. Furthermore, the peer robot was perceived as friendlier, more social, smarter, and more respectful than the tutor robot, regardless of initial skill level.more » « less
-
We aim to design robotic educational support systems that can promote socially and intellectually meaningful learning experiences for students while they complete school work outside of class. To pursue this goal, we conducted participatory design studies with 10 children (aged 10–12) to explore their design needs for robot assisted homework. We investigated children’s current ways of doing homework, the type of support they receive while doing homework, and co-designed the speech and expressiveness of a homework companion robot. Children and parents attending our design sessions explained that an emotionally expressive social robot as a homework aid can support students’ motivation and engagement, as well as their affective state. Children primarily perceived the robot as a dedicated assistant at home, capable of forming meaningful friendships, or a shared classroom learning resource. We present key design recommendations to support students’ homework experiences with a learning companion robot.more » « less
-
Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the ATPOMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student’s mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n = 28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3173386.3176979
