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An agent-based model for teaching–learning processes

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  • Ormazábal, Ignacio
  • Borotto, Félix A.
  • Astudillo, Hernán F.

Abstract

This paper presents an agent-based model for describing the increase the knowledge by accumulating the information needed to complete a learning task or objectives, based on phenomena studied by behavioral and learning scientists. From the simulations, the average increase rate in knowledge, the skewness and kurtosis of knowledge distributions, and grade distributions are determined. These tools make it possible to evaluate the efficiency of teaching strategies and the performance of learning in the classroom. The present model significantly reproduces the phenomenology obtained in Bordogna and Albano (2001), showing first and second-order phase transitions and the temporal dynamics of knowledge. Furthermore, the results of our model allow us to built a gas model analogy. Some of the study cases show characteristics of systems far from the state of thermodynamic equilibrium. This allows us to use the known techniques from gas models to interpret the dynamics of the simulated learning process. The presented model does not describe the teaching–learning process in all its complexity. We use a simple behavioral characteristic of the persons, namely, the inattentional private experience. However, it allows us to prove learning strategies to optimize the learning process. Also, this model is a starting point to propose new models with more elements from neuroscience and sociophysics to study in greater depth the dynamics of the classroom.

Suggested Citation

  • Ormazábal, Ignacio & Borotto, Félix A. & Astudillo, Hernán F., 2021. "An agent-based model for teaching–learning processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    • Handle: RePEc:eee:phsmap:v:565:y:2021:i:c:s037843712030861x
    DOI: 10.1016/j.physa.2020.125563
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    References listed on IDEAS

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    1. Clelia M. Bordogna & Ezequiel V. Albano, 2001. "Phase Transitions In A Model For Social Learning Via The Internet," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 12(08), pages 1241-1250.
    2. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: II. Agent-based models," Post-Print hal-00621059, HAL.
    3. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, April.
    4. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: II. Agent-based models," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1013-1041.
    5. Schelling, Thomas C, 1969. "Models of Segregation," American Economic Review, American Economic Association, vol. 59(2), pages 488-493, May.
    6. Ismo T. Koponen & Maija Nousiainen, 2018. "An Agent-Based Model of Discourse Pattern Formation in Small Groups of Competing and Cooperating Members," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(2), pages 1-1.
    7. Ignacio Ormazábal & F. A. Borotto & H. F. Astudillo, 2017. "Influence of Money Distribution on Civil Violence Model," Complexity, Hindawi, vol. 2017, pages 1-15, November.
    8. Bordogna, Clelia M. & Albano, Ezequiel V., 2003. "Simulation of social processes: application to social learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 329(1), pages 281-286.
    9. Victor M. Yakovenko & J. Barkley Rosser, 2009. "Colloquium: Statistical mechanics of money, wealth, and income," Papers 0905.1518, arXiv.org, revised Dec 2009.
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    Cited by:

    1. Philippe Collard, 2022. "The “flat peer learning” agent-based model," Journal of Computational Social Science, Springer, vol. 5(1), pages 161-187, May.

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