The potential of learning from erroneous models: comparing three types of model instruction

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Abstract

Learning from computer models is a promising approach to learning. This study investigated how three types of learning from computer models can be applied to teach high-school students (aged 14–17) about the process of glucose–insulin regulation. Two traditional forms of learning from models (i.e. simulating a predefined model and constructing a model) were compared to learning from an erroneous model. In this innovative form of learning from computer models, students are provided with a model that contained errors to be corrected. As such, students do not have to engage in the difficult task of constructing a model. Rather, they are challenged to work with and correct the model in order for the simulation to generate correct output. As predicted, learning from erroneous models enhances learning of domain-specific knowledge better than running a simulation or constructing a model. Copyright © 2016 System Dynamics Society

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