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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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    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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