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International Conference on Artificial Intelligence in Education

AIED 2018: Artificial Intelligence in Education pp 420–435Cite as

Predicting the Temporal and Social Dynamics of Curiosity in Small Group Learning

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

Abstract

Curiosity is an intrinsic motivation for learning, but is highly dynamic and changes moment to moment in response to environmental stimuli. In spite of the prevalence of small group learning in and outside of modern classrooms, little is known about the social nature of curiosity. In this paper, we present a model that predicts the temporal and social dynamics of curiosity based on sequences of behaviors exhibited by individuals engaged in group learning. This model reveals distinct sequential behavior patterns that predict increase and decrease of curiosity in individuals, and convergence to high and low curiosity among group members. In particular, convergence of the entire group to a state of high curiosity is highly correlated with sequences of behaviors that involve the most social of group behaviors - such as questions and answers, arguments and sharing findings, as well as scientific reasoning behaviors such as hypothesis generation and justification. The implications of these findings are discussed for educational systems that intend to evoke and scaffold curiosity in group learning contexts.

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Notes

  1. 1.

    Experimental setup at https://tinyurl.com/experimental-setup.

  2. 2.

    We remove raters who take less than 1.5 std. deviation time to rate and used inverse-based bias correlation to counter label over- & under-use.

  3. 3.

    0.72 Cronbach’s alpha intra-class correlation.

  4. 4.

    Outlined in [32].

  5. 5.

    Coding scheme for verbal and non-verbal behaviors at http://tinyurl.com/codingschemecuriosity.

  6. 6.

    Facial-landmark feature coding and classification heuristics at https://tinyurl.com/curiositynonverbal.

  7. 7.

    Event mining is robust as we use z-score-based thresholds to select individual and group specific intervals.

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Acknowledgement

We would like to thank the Heinz Endowment, all student interns, teachers and instructors of local school and summer camps, as well our collaborators Dr. Jessica Hammer, Dr. Louis-Phillppe Morency, Dr. Geoff Kaufman and Alexandra To for supporting the SCIPR project.

Author information

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Bhargavi Paranjape, Zhen Bai & Justine Cassell

Authors
  1. Bhargavi Paranjape
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  2. Zhen Bai
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  3. Justine Cassell
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Corresponding author

Correspondence to Bhargavi Paranjape .

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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Paranjape, B., Bai, Z., Cassell, J. (2018). Predicting the Temporal and Social Dynamics of Curiosity in Small Group Learning. 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_31

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  • DOI: https://doi.org/10.1007/978-3-319-93843-1_31

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  • Online ISBN: 978-3-319-93843-1

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