An Adaptive Teachable Robot For Encouraging Teamwork

Social robots used in education can take different roles, including tutor robots and peer robots. Peer robots (also called teachable robots) take the role of a novice in a teaching interaction while the students take the role of the teacher. Teachable robots leverage learning by teaching, which has been shown in prior research to increase the students’ learning effort and time spent on the learning activity, leading to enhanced student learning. The concept of teachable robots has previously been applied for one-to-one interaction, however, to date, few studies use teachable robots in a group setting. In this thesis, we developed an adaptive learning algorithm for a teachable robot that encourages a group of students to discuss their thoughts and teaching decisions during the tutoring session. We hypothesize that the robot's encouragement of group discussion can enhance the social engagement of group members, leading to improved task engagement, learning and enjoyment. The robot adapts to the students' talking activity and adjusts the frequency and type of encouragement. The robot uses reinforcement learning to maximise interaction between the students. The proposed approach was validated through a series of studies. The first pilot study was performed in an elementary school and observed the interactions between groups of students and teachable robots. The main study investigated the feasibility of an adaptive encouraging robot in a remote setting. We recruited 68 adults, who worked together in pairs online on a web application called Curiosity Notebook to teach a humanoid robot about the classification of rocks and minerals. We measured social engagement based on the communication between group-mates, while the metric for task engagement was generated based on the users’ activities in the Curiosity Notebook. The results show that the adaptive robot was successful in creating more dialogue between group members and in increasing task engagement, but did not affect learning or enjoyment. Over time, the adaptive robot was also able to encourage both members to contribute more equally to the conversation

The Effects of Physical Form and Embodied Action in a Teachable Robot for Geometry Learning

A teachable agent is a learning companion that students teach about a domain they are trying to master. While most teachable agents have been virtual, there may be advantages to having students teach an agent with a physical form (i.e., a robot). The robot may better engage students in the learning activity, and if students take embodied action in order to instruct the robot, they may develop deeper knowledge. In this paper, we investigate these two hypotheses using the rTAG system, a teachable robot for geometry learning. In a study with 37 4th-6th grade participants, we compare rTAG to two other conditions, one where students use embodied action to teach a virtual agent, and one where students teach a virtual agent on a personal computer. We find that while there are no significant learning differences between conditions, students' perceptions of the agent are influenced by condition and prior knowledge

Producing Acoustic-Prosodic Entrainment in a Robotic Learning Companion to Build Learner Rapport

abstract: With advances in automatic speech recognition, spoken dialogue systems are assuming increasingly social roles. There is a growing need for these systems to be socially responsive, capable of building rapport with users. In human-human interactions, rapport is critical to patient-doctor communication, conflict resolution, educational interactions, and social engagement. Rapport between people promotes successful collaboration, motivation, and task success. Dialogue systems which can build rapport with their user may produce similar effects, personalizing interactions to create better outcomes. This dissertation focuses on how dialogue systems can build rapport utilizing acoustic-prosodic entrainment. Acoustic-prosodic entrainment occurs when individuals adapt their acoustic-prosodic features of speech, such as tone of voice or loudness, to one another over the course of a conversation. Correlated with liking and task success, a dialogue system which entrains may enhance rapport. Entrainment, however, is very challenging to model. People entrain on different features in many ways and how to design entrainment to build rapport is unclear. The first goal of this dissertation is to explore how acoustic-prosodic entrainment can be modeled to build rapport. Towards this goal, this work presents a series of studies comparing, evaluating, and iterating on the design of entrainment, motivated and informed by human-human dialogue. These models of entrainment are implemented in the dialogue system of a robotic learning companion. Learning companions are educational agents that engage students socially to increase motivation and facilitate learning. As a learning companion’s ability to be socially responsive increases, so do vital learning outcomes. A second goal of this dissertation is to explore the effects of entrainment on concrete outcomes such as learning in interactions with robotic learning companions. This dissertation results in contributions both technical and theoretical. Technical contributions include a robust and modular dialogue system capable of producing prosodic entrainment and other socially-responsive behavior. One of the first systems of its kind, the results demonstrate that an entraining, social learning companion can positively build rapport and increase learning. This dissertation provides support for exploring phenomena like entrainment to enhance factors such as rapport and learning and provides a platform with which to explore these phenomena in future work.Dissertation/ThesisDoctoral Dissertation Computer Science 201