The Impact of an In-Home Co-Located Robotic Coach in Helping People Make Fewer Exercise Mistakes

Salomons, Nicole; Wallenstein, Tom; Ghose, Debasmita; Scassellati, Brian

doi:10.1109/ro-man53752.2022.9900722

Cited by 12 publications

(1 citation statement)

References 23 publications

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“…A robot can enhance an ITS by providing a social presence during the interaction. Compared to a screen system only, embodied robot tutors have been shown to cause greater compliance Bainbridge et al (2011) , higher learning gains Leyzberg et al (2012) , more engagement Wainer et al (2007) , and fewer mistakes Salomons et al (2022b) . A robot can also interact with the user as a peer or tutee rather than as the traditional teacher Salomons et al, 2022a ; Chen et al, 2020 .…”

Section: Introductionmentioning

confidence: 99%

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

Salomons,

Scassellati

2024

Front. Robot. AI

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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.

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