Personalizing robot tutors to individuals' learning differences
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. Tutored students scored in the 95th percen...
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| Published in: | HRI '14 : proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction : March 3-6, 2014, Bielefeld, Germany pp. 423 - 430 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
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New York, NY, USA
ACM
03.03.2014
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| Series: | ACM Conferences |
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| ISBN: | 1450326587, 9781450326582 |
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| 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. 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. |
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| AbstractList | 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 95 th 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 (68 th 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. 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. 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. |
| Author | Spaulding, Samuel Leyzberg, Daniel Scassellati, Brian |
| Author_xml | – sequence: 1 givenname: Daniel surname: Leyzberg fullname: Leyzberg, Daniel email: daniel.leyzberg@yale.edu organization: Yale University, New Haven, CT, USA – sequence: 2 givenname: Samuel surname: Spaulding fullname: Spaulding, Samuel email: samuelsp@media.mit.edu organization: Massachusetts Institute of Technology, Cambridge, MA, USA – sequence: 3 givenname: Brian surname: Scassellati fullname: Scassellati, Brian email: scaz@cs.yale.edu organization: Yale University, New Haven, CT, USA |
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| Keywords | assessment personalization education robotics its tutoring hri |
| Language | English |
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| Snippet | In education research, there is a widely-cited result called "Bloom's two sigma" that characterizes the differences in learning outcomes between students who... |
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| SubjectTerms | Applied computing -- Law, social and behavioral sciences -- Psychology Assessment Computer systems organization -- Embedded and cyber-physical systems -- Robotics Computing methodologies -- Artificial intelligence -- Control methods -- Robotic planning Computing methodologies -- Artificial intelligence -- Planning and scheduling -- Robotic planning Education Educational robots Games HRI Human-robot interaction ITS Personalization Robot kinematics Robotics Tutoring |
| Title | Personalizing robot tutors to individuals' learning differences |
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