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Automated Pitch Convergence Improves Learning in a Social, Teachable Robot for Middle School Mathematics

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Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10947))

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Abstract

Pedagogical agents have the potential to provide not only cognitive support to learners but socio-emotional support through social behavior. Socio-emotional support can be a critical element to a learner’s success, influencing their self-efficacy and motivation. Several social behaviors have been explored with pedagogical agents including facial expressions, movement, and social dialogue; social dialogue has especially been shown to positively influence interactions. In this work, we explore the role of paraverbal social behavior or social behavior in the form of paraverbal cues such as tone of voice and intensity. To do this, we focus on the phenomenon of entrainment, where individuals adapt their paraverbal features of speech to one another. Paraverbal entrainment in human-human studies has been found to be correlated with rapport and learning. In a study with 72 middle school students, we evaluate the effects of entrainment with a teachable robot, a pedagogical agent that learners teach how to solve ratio problems. We explore how a teachable robot which entrains and introduces social dialogue influences rapport and learning; we compare with two baseline conditions: a social condition, in which the robot speaks socially, and a non-social condition, in which the robot neither entrains nor speaks socially. We find that a robot that does entrain and speaks socially results in significantly more learning.

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Notes

  1. 1.

    Survey questions can be found at www.public.asu.edu/~nlubold/surveys/nico_rapport.pdf

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Acknowledgements

This work is supported by the National Robotics Initiative and the National Science Foundation, grant # CISE-IIS-1637809. We would also like to thank Samantha Baker and Ishrat Ahmed for their help in setting up this study and the data collection process.

Author information

Authors and Affiliations

  1. Arizona State University, Tempe, AZ, 85281, USA

    Nichola Lubold & Erin Walker

  2. Mount Holyoke College, South Hadley, MA, 01075, USA

    Heather Pon-Barry

  3. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Amy Ogan

Authors
  1. Nichola Lubold
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  2. Erin Walker
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  3. Heather Pon-Barry
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  4. Amy Ogan
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Corresponding author

Correspondence to Nichola Lubold .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Penstein Rosé

  2. University of Technology, Sydney, NSW, Australia

    Roberto Martínez-Maldonado

  3. University of Duisburg-Essen, Duisburg, Germany

    H. Ulrich Hoppe

  4. UCL Institute of Education, London, UK

    Rose Luckin

  5. UCL Institute of Education, London, UK

    Manolis Mavrikis

  6. UCL Institute of Education, London, UK

    Kaska Porayska-Pomsta

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  8. University of Sussex, Brighton, UK

    Benedict du Boulay

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Lubold, N., Walker, E., Pon-Barry, H., Ogan, A. (2018). Automated Pitch Convergence Improves Learning in a Social, Teachable Robot for Middle School Mathematics. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-93843-1_21

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