Learning from rediscovering system dynamics models

Authors

  • Martin SCHAFFERNICHT Universidad de Talca, Facultad de Ciencias Empresariales

Keywords:

system dynamics, interactive learning environment, modeling, model exploration, discovery learning

Abstract

This article deals with learning from the exploration of system dynamics models.  System dynamics modeling intends to improve judgment and decision, but is very time consuming.  Model-based interactive learning environments allow saving time, but critics doubt the effectiveness for deep learning.  The question is if there is a third way in-between.  Relevant examples from system dynamics are analyzed to identify the key activities that trigger learning; they are organized as a structured exploration process, making learners ask relevant questions, obtain valid responses and correctly interpret them.  Based upon this, a process for guided rediscovery is proposed together with guidelines for the functional properties of a “systemic exploratory”.  Guided rediscovery enables non-specialists to gain relevant insights into dynamically complex situations and is a tool for decision policy design.

Author Biography

Martin SCHAFFERNICHT, Universidad de Talca, Facultad de Ciencias Empresariales

Martin SCHAFFERNICHT est professeur à la Facultad de Ciencias Empresariales de l’Université de Talca au Chili.  Sa thèse sous la direction de Robert Reix s’est consacrée au sujet de l’apprentissage dans les organisations.  Depuis, sa recherche porte sur la relation entre modélisation et apprentissage du point de vue de la dynamique des systèmes.

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How to Cite

SCHAFFERNICHT, M. (2009). Learning from rediscovering system dynamics models. Systèmes d’Information Et Management (French Journal of Management Information Systems), 14(4), 87–105. Retrieved from https://revuesim.org/index.php/sim/article/view/294

Issue

Vol. 14 No. 4 (2009): Special Issue on "Simulation"

Section

Empirical research

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