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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Vydáno v:HRI '14 : proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction : March 3-6, 2014, Bielefeld, Germany s. 423 - 430
Hlavní autoři: Leyzberg, Daniel, Spaulding, Samuel, Scassellati, Brian
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 03.03.2014
Edice:ACM Conferences
Témata:
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
assessment
personalization
education
robotics
its
tutoring
hri
ISBN:1450326587, 9781450326582
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Shrnutí: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.
ISBN:1450326587
9781450326582
DOI:10.1145/2559636.2559671