Personalizing robot tutors to individuals' learning differences

Daniel Noah Leyzberg, Samuel Spaulding, Brian Scassellati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

93 Scopus citations

Abstract

In education research, there is a widely-cited result called "Bloom's two sigma" that characterizes the differences in learning outcomes between students who receive one-on-one tutoring and those who receive traditional classroom instruction [1]. Tutored students scored in the 95th percentile, or two sigmas above the mean, on average, compared to students who received traditional classroom instruction. In human-robot interaction research, however, there is relatively little work exploring the potential benefits of personalizing a robot's actions to an individual's strengths and weaknesses. In this study, participants solved grid-based logic puzzles with the help of a personalized or non-personalized robot tutor. Participants' puzzle solving times were compared between two non-personalized control conditions and two personalized conditions (n=80). Although the robot's personalizations were less sophisticated than what a human tutor can do, we still witnessed a "one-sigma" improvement (68th percentile) in post-tests between treatment and control groups. We present these results as evidence that even relatively simple personalizations can yield significant benefits in educational or assistive human-robot interactions.

Original languageEnglish (US)
Title of host publicationHRI 2014 - Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages423-430
Number of pages8
ISBN (Print)9781450326582
DOIs
StatePublished - Jan 1 2014
Externally publishedYes
Event9th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2014 - Bielefeld, Germany
Duration: Mar 3 2014Mar 6 2014

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Conference

Conference9th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2014
CountryGermany
CityBielefeld
Period3/3/143/6/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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  • Cite this

    Leyzberg, D. N., Spaulding, S., & Scassellati, B. (2014). Personalizing robot tutors to individuals' learning differences. In HRI 2014 - Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction (pp. 423-430). (ACM/IEEE International Conference on Human-Robot Interaction). IEEE Computer Society. https://doi.org/10.1145/2559636.2559671