Dissertation Defense - Dan Leyzberg
Creating Personalized Robot Tutors That Adapt to The Needs of Individual Students
Committee Members:
Brian Scassellati (Advisor)
Dana Angluin
Drew McDermott
Andrea Thomaz (Georgia Tech)
Abstract: This dissertation makes three contributions to the study of personalization in robot tutoring: (1) we provide evidence for improved student learning gains associated with the physical presence of a robot tutor, (2) we deliver experimentally-derived design guidelines for future work in robot tutoring, and (3) we provide novel robot tutoring personalization systems and demonstrate that these systems improve student learning outcomes over non-personalized systems by 1.2 to 2.0 standard deviations, corresponding to gains in the 88th to 98th percentile.
We begin by investigating a foundational question in the field of robot tutoring: can the physical presence of a robot tutor affect student learning outcomes? We conducted an experiment comparing student learning outcomes between three conditions in which participants received tutoring from either: (1) a physically-embodied robot tutor, (2) an on-screen tutor, or (3) a voice-only tutor. We found that students who received tutoring from the physically-embodied robot tutor were more engaged in the lessons than students in the other two conditions. We also found that, despite the instructional content being the same across all three conditions, students who received tutoring from the physically-embodied robot achieved significantly better learning outcomes than students in the other two groups by 0.3 standard deviations, corresponding to gains in the 62nd percentile.
In order to arrive at design guidelines for our work in automated personalization for robot tutoring, we first studied how humans personalize their tutoring. To do this, we asked participants to teach robot students, which, unlike human students, can be expected to behave in the exact same way on multiple occasions and with different human tutors. By employing robots as students, we were able to study the nuances of human tutoring personalization. We found that human tutors teach more and produce more strongly affective vocalizations to students who are less successful than to students who are more successful. We also found that, even if two students perform exactly the same on all learning tasks, human tutors still personalize their instruction based on the affective content of students’ responses. We use these findings to propose guidelines for future work in automated personalization, with the goal of producing more human-like automated tutoring.
Our final contributions are our automated personalization systems for robot tutors: two of which are intended for shorter-term robot tutoring interactions and one of which is intended for longer-term interactions. For the shorter-term models, designed for use in at most one contiguous session with a robot tutor, we created an additive model intended to investigate the effects of the simplest forms of personalization systems, and a Bayesian model that is slightly more sophisticated and leads to improved learning gains over the additive model. For the longer-term system, we used a Hidden Markov Model (HMM) that tracked students over the course of five sessions, taking place over two weeks. We evaluated these systems against similar non-personalized systems with human students and found that our personalization systems increased learning gains by between 1.2 and 2.0 standard deviations over non-personalized systems, corresponding to gains in the 88th to 98th percentiles.
Draft: