Time-dependant Bayesian knowledge tracing-Robots that model user skills over time

Creating an accurate model of a user's skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user's knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or multiple-choice questions, where the user's model is only updated upon task completion. These tasks have a single correct answer and they generate an unambiguous observation of the user's answer. This is not the case for more complex tasks such as programming or engineering tasks, where the user completing the task creates a succession of noisy user observations as they work on different parts of the task. We create an algorithm called Time-Dependant Bayesian Knowledge Tracing (TD-BKT) that tracks users' skills throughout these more complex tasks. We show in simulation that it has a more accurate model of the user's skills and, therefore, can select better teaching actions than previous algorithms. Lastly, we show that a robot can use TD-BKT to model a user and teach electronic circuit tasks to participants during a user study. Our results show that participants significantly improved their skills when modeled using TD-BKT.

A Minority of One against a Majority of Robots

Studies have shown that people conform their answers to match those of group members even when they believe the group’s answer to be wrong [2]. In this experiment, we test whether people conform to groups of robots and whether the robots cause informational conformity (believing the group to be correct), normative conformity (feeling peer pressure), or both. We conducted an experiment in which participants (N = 63) played a subjective game with three robots. We measured humans’ conformity to robots by how many times participants changed their preliminary answers to match the group of robots’ in their final answer. Participants in conditions that were given more information about the robots’ answers conformed significantly more than those who were given less, indicating that informational conformity is present. Participants in conditions where they were aware they were a minority in their answers conformed more than those who were unaware they were a minority. Additionally, they also report feeling more pressure to change their answers from the robots, and the amount of pressure they reported was correlated to the frequency they conformed, indicating normative conformity. Therefore, we conclude that robots can cause both informational and normative conformity in people.

Improving social skills in children with ASD using a long-term, in-home social robot

Social robots can offer tremendous possibilities for autism spectrum disorder (ASD) interventions. To date, most studies with this population have used short, isolated encounters in controlled laboratory settings. Our study focused on a 1-month, home-based intervention for increasing social communication skills of 12 children with ASD between 6 and 12 years old using an autonomous social robot. The children engaged in a triadic interaction with a caregiver and the robot for 30 min every day to complete activities on emotional storytelling, perspective-taking, and sequencing. The robot encouraged engagement, adapted the difficulty of the activities to the child's past performance, and modeled positive social skills. The system maintained engagement over the 1-month deployment, and children showed improvement on joint attention skills with adults when not in the presence of the robot. These results were also consistent with caregiver questionnaires. Caregivers reported less prompting over time and overall increased communication.