Sociohydrologic Systems Thinking: An Analysis of Undergraduate Students’ Operationalization and Modeling of Coupled Human-Water Systems
Sociohydrologic Systems Thinking: An Analysis of Undergraduate Students’ Operationalization and Modeling of Coupled Human-Water Systems
复制标题
社会水文系统思维：本科生人水耦合系统操作与建模分析
作者：
Diane Lally;C. Forbes
科学和环境素养的关键之一是系统思维。学习如何思考系统之间的相互作用，系统的深远影响以及系统的动态特性都是科学学习的重要成果。然而，学生需要支持，以发展系统思维能力在本科地球科学教室。虽然以系统思维为中心的教学有可能使学生的学习受益，但我们对学生使用系统思维来操作和建模SHS的理解存在差距，以及他们对系统思维的元认知评价。为了满足这一需求，我们设计，实施，完善和研究了一个入门级的，跨学科的课程，重点是耦合人水，或社会水文，系统。本研究的数据来自三个连续迭代的过程中，涉及学生模型和解释的社会水文问题（n = 163）。为了分析这些数据，我们计算了绘制模型的主题特征，并将操作化规则应用于书面回答。对书面解释的分析显示，系统思维的潜在类别之间存在显著差异（F（5，768）= 401.6，p < 0.05）。学生最能操作他们的系统思考问题识别（M = 2.22，SD = 0.73）相比，意外的后果（M = 1.43，SD = 1.11）。学生生成的系统思维模型揭示了系统组件、模式和机制之间的统计学显著差异，F（2，132）= 3.06，p < 0.05。与相关过程或机制相比，学生最关注系统组件（M = 13.54，SD = 7.15）。定性数据表明三种类型的模型限制，包括范围/规模，时间，和特定的组件/机制/模式排除。这些发现对支持本科地球科学课堂的系统思维以及深入了解这两种技能之间的联系具有重要意义。
One of the keys to science and environmental literacy is systems thinking. Learning how to think about the interactions between systems, the far-reaching effects of a system, and the dynamic nature of systems are all critical outcomes of science learning. However, students need support to develop systems thinking skills in undergraduate geoscience classrooms. While systems thinking-focused instruction has the potential to benefit student learning, gaps exist in our understanding of students’ use of systems thinking to operationalize and model SHS, as well as their metacognitive evaluation of systems thinking. To address this need, we have designed, implemented, refined, and studied an introductory-level, interdisciplinary course focused on coupled human-water, or sociohydrologic, systems. Data for this study comes from three consecutive iterations of the course and involves student models and explanations for a socio-hydrologic issue (n = 163). To analyze this data, we counted themed features of the drawn models and applied an operationalization rubric to the written responses. Analyses of the written explanations reveal statistically-significant differences between underlying categories of systems thinking (F(5, 768) = 401.6, p < 0.05). Students were best able to operationalize their systems thinking about problem identification (M = 2.22, SD = 0.73) as compared to unintended consequences (M = 1.43, SD = 1.11). Student-generated systems thinking models revealed statistically significant differences between system components, patterns, and mechanisms, F(2, 132) = 3.06, p < 0.05. Students focused most strongly on system components (M = 13.54, SD = 7.15) as compared to related processes or mechanisms. Qualitative data demonstrated three types of model limitation including scope/scale, temporal, and specific components/mechanisms/patterns excluded. These findings have implications for supporting systems thinking in undergraduate geoscience classrooms, as well as insight into links between these two skills.