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Providing structural transparency when exploring a model's behavior: Effects on performance and knowledge acquisition

机译：探索模型行为时提供结构透明性：对性能和知识获取的影响

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Prior exploration is an instructional strategy which has improved performance and knowledge acquisition in system-dynamics based learning environments, but only to a limited degree. This study investigates whether model transparency, showing users the internal structure of models, can extend the prior exploration strategy and improve learning even more. In an experimental study, participants in a web-based simulation learned about and managed a small developing nation. All participants were provided the prior exploration strategy but only half received prior exploration embedded in a structure-behavior diagram intended to make the underlying model's structure more transparent. Participants provided with the more transparent strategy demonstrated better knowledge acquisition of the underlying model on an objective measure (multiple-choice posttest) but no difference on a subjective measure (open-ended verbal protocols based on short essay questions). Furthermore, their performance (managing the nation) was the equivalent to those in the less transparent condition. Combined with our previous studies, the results suggest that while prior exploration is a beneficial strategy for both performance and knowledge acquisition, making the model structure transparent in this way (with structure-behavior diagrams) is more limited in its effect.
机译：先前的探索是一种教学策略，它在基于系统动力学的学习环境中提高了性能和知识获取能力，但仅限于有限的程度。本研究调查模型透明性（向用户显示模型的内部结构）是否可以扩展先前的探索策略并进一步改善学习效果。在一项实验研究中，基于网络的模拟的参与者了解并管理了一个小型发展中国家。为所有参与者提供了先验探索策略，但只有一半的参与者接受了嵌入在结构行为图中的先验探索，目的是使基础模型的结构更透明。具有更透明策略的参与者在客观度量（多项选择后测）上展示了对基础模型的更好的知识获取，而在主观度量（基于短文问题的开放式口头协议）上则没有差异。此外，他们的表现（管理国家）与透明度较低的情况相当。结合我们先前的研究，结果表明，尽管先前的探索对于性能和知识获取都是有益的策略，但以这种方式使模型结构透明（带有结构行为图）的效果受到更大的限制。

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