The system that shapes a problem can be represented using a map, in which relevant constructs are listed as nodes, and salient interrelationships are provided as directed edges which track the direction of causation. Such representations are particularly useful to address complex problems which are multi-factorial and may involve structures such as loops, in contrast with simple problems which may have a clear root cause and a short chain of causes-and-effects. Although students are often evaluated based on either simple problems or simplified situations (e.g., true/false, multiple choice), they need systems thinking skills to eventually deal with complex, open-ended problems in their professional lives. A starting point is thus to construct a representation of the problem space, such as a causal map, and then to identify and contrast solutions by navigating this map. The initial step of abstracting a system into a map is challenging for students: unlike seasoned experts, they lack a detailed understanding of the application domain, and hence struggle in capturing its key concepts and interrelationships. Case libraries can remedy this disadvantage, as they can transfer the knowledge of experts to novices. However, the content of the cases can impact the perspectives of students. For example, their understanding of a system (as reflected in a map) may differ when they are exposed to case studies depicting successful or failed interventions in a system. Previous studies have abundantly documented that cases can support students, using a variety of metrics such as test scores. In the present study, we examine the ways in which the representation of a system (captured as a causal map) changes as a function of exposure to certain types of evidence. Our experiments across three cohorts at two institutions show that providing students with cases tends to broaden their coverage of the problem space, but the knowledge afforded by the cases is integrated in the students’ maps differently depending on the type of case, as well as the cohort of students.

中文翻译：

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基于正负证据暴露的系统视角如何变化

可以使用地图来表示塑造问题的系统，其中将相关构造列为节点，并提供显着的相互关系作为跟踪因果关系方向的有向边。与可能具有明确的根本原因和因果关系短的简单问题相比，这种表示形式对于解决多因素的复杂问题特别有用，而这些复杂问题可能涉及诸如循环之类的结构。尽管通常根据简单问题或简化情况（例如，对/错，多项选择）对学生进行评估，但他们仍需要系统思维能力，才能最终解决其职业生涯中复杂的，开放性的问题。因此，首先要构建问题空间的表示形式，例如因果图，然后通过导航该地图来识别和对比解决方案。将系统抽象到地图中的第一步对学生而言是具有挑战性的：与经验丰富的专家不同，他们缺乏对应用程序领域的详细了解，因此难以捕捉其关键概念和相互关系。案例库可以弥补这一缺点，因为它们可以将专家的知识转移给新手。但是，案例的内容会影响学生的观点。例如，当他们接触描述系统成功或失败干预的案例研究时，他们对系统的理解（如地图所示）可能会有所不同。先前的研究已经大量记录了案例可以使用各种指标（例如考试成绩）来支持学生。在目前的研究中，我们研究了系统表示形式（捕获为因果图）随暴露于某些类型的证据而变化的方式。我们在两个机构的三个队列中进行的实验表明，为学生提供案例往往会扩大他们对问题空间的覆盖范围，但是案例的提供知识会根据案例类型以及案例类型的不同而不同地整合到学生的地图中。学生队列。