Ms. An (Meeting Students' Academic Needs): Engaging Students in Math Education

This research presents a new, socially adaptive robot tutor, Ms. An (Meeting Students' Academic Needs). The goal of this research was to use a decision tree model to develop a socially adaptive robot tutor that predicted and responded to student emotion and performance to actively engage students in mathematics education. The novelty of this multi-disciplinary project is the combination of the fields of HRI, AI, and education. In this study we (1) implemented a decision tree model to classify student emotion and performance for use in adaptive robot tutoring-an approach not applied to educational robotics; (2) presented an intuitive interface for seamless robot operation by novice users; and (3) applied direct human teaching methods (guided practice and progress monitoring) for a robot tutor to engage students in mathematics education. Twenty 4th and 5th grade students in rural South Carolina participated in a between subjects study with two conditions: (A) with a non-adaptive robot (control group); and (B) with a socially adaptive robot (adaptive group). Students engaged in two one-on-one tutoring sessions to practice multiplication per the South Carolina 4th and 5th grade mathematics state standards. Although our decision tree models were not very predictive, the results gave answers to our current questions and clarity for future directions. Our adaptive strategies to engage students academically were effective. Further, all students enjoyed working with the robot and we did not see a difference in emotional engagement across the two groups.