The benefits of personalized social robots must be evaluated in real-world educational contexts over periods of time longer than a single session to understand their full potential to impact learning outcomes. In this work, we describe a personalization system designed for longer-term personalization that orders curriculum based on an adaptive Hidden Markov Model (HMM) that evaluates students' skill proficiencies. We present a study investigating the effectiveness of this system in a five-session interaction with a robot tutor, taking place over the course of 2 weeks. Our system is evaluated in the context of native Spanish-speaking first-graders interacting with a social robot tutor while completing an English Language Learning educational task. Participants either received lessons: (1) ordered by our adaptive HMM personalization system which selects a lesson based on a skill that the individual participant needs more practice with ("personalized condition") or (2) ordered randomly from among the lessons the participant had not yet seen ("non-personalized condition"). We found that participants who received personalized lessons from the robot tutor outperformed participants who received non-personalized lessons on a post-test by 2.0 standard deviations on average, corresponding to a mean learning gain in the 98th percentile.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Artificial Intelligence
- English Language Learning (ELL)
- Human-robot interaction