A Preliminary Analysis of Learners Social Behavioral Cues When Interacting with Language Learning Robots and IoT Based Toys This paper reports a pilot study of an ongoing project focusing on analyzing learnersu0027 social behavioral cues as learners interact with an English learning environment constructed by language learning robots and IoT based toys. In accordance with Vygotskyu0027s sociocultural perspective that learning process occurs when learners interact with the external world, this study observes and analyzes learning process taking place in the realworld learning situations through the lens of social behavioral cues. A qualitative method of interaction analysis is used to understand learnersu0027 social behavioral cues when they interact with robots, IoT based toys, and other persons or objects present at the English learning environment. With interaction analysis, this study aims at identifying what kind of social behavioral cues observed in the English learning environment. The frequently observed social behavioral cues reflect learnersu0027 learning situation and inform the design of learning activities and the development of learning technologies. With such findings, this study hopes to initiate the awareness and endeavors to facilitate learnersu0027 learning by supporting diverse types of social interaction when they engage in the learning activities with robots and IoT based toys.

Teaching and learning with children: Impact of reciprocal peer learning with a social robot on childrenxe2x80x99s learning and emotive engagement Abstract   Pedagogical agents are typically designed to take on a single role: either as a tutor who guides and instructs the student, or as a tutee that learns from the student to reinforce what heshe knows. While both agentrole paradigms have been shown to promote student learning, we hypothesize that there will be heightened benefit with respect to studentsxe2x80x99 learning and emotional engagement if the agent engages children in a more peerlike way xe2x80x94 adaptively switching between tutortutee roles. In this work, we present a novel active roleswitching (ARS) policy trained using reinforcement learning, in which the agent is rewarded for adapting its tutor or tutee behavior to the childxe2x80x99s knowledge mastery level. To investigate how the three different childxe2x80x93agent interaction paradigms (tutee, tutor, and peer agents) impact childrenxe2x80x99s learning and affective engagement, we designed a randomized controlled betweensubject experiment. Fiftynine children aged 5xe2x80x937 years old from a local public school participated in a collaborative wordlearning activity with one of the three agentrole paradigms. Our analysis revealed that childrenxe2x80x99s vocabulary acquisition benefited from the robot tutorxe2x80x99s instruction and knowledge demonstration, whereas children exhibited slightly greater affect on their faces when the robot behaves as a tutee of the child. This synergistic effect between tutor and tutee roles suggests why our adaptive peerlike agent brought the most benefit to childrenxe2x80x99s vocabulary learning and affective engagement, as compared to an agent that interacts only as a tutor or tutee for the child. This work sheds light on how fixed role (tutortutee) and adaptive role (peer) agents support childrenxe2x80x99s cognitive and emotional needs as they play and learn. It also contributes to an important new dimension of designing educational agents xe2x80x94 actively adapting roles based on the studentxe2x80x99s engagement and learning needs.