Ms. An (Meeting Students’ Academic Needs): A socially adaptive robot Tutor for Student Engagement in Math Education

Tuesday, March 27, 2018 - 10:00am to 11:00am
Meeting room 2265, Innovation Center

DISSERTATION DEFENSE

Author : Karina Liles
Advisor : Dr. Jenay Beer

Abstract

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 math 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 math 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 math state standards.
Although our decision tree models were less optimal than we had hoped, 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. Our adaptive strategies made students think more deeply about their work and focus more.
This study offered insight for developing a socially adaptive robot tutor to engage students academically and emotionally while practicing multiplication. Results from this study will inform the human-robot interaction (HRI) and artificial intelligence (AI) communities on best practices and techniques within the scope of this work.

Date : March 27th, 2018
Time : 10:00 am
Place : Meeting room 2265, Innovation Center