论文标题
对科学和工程专家解决问题过程的详细表征；教学指南
A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering; Guidance for Teaching and Assessment
论文作者
论文摘要
科学和工程学（S＆E）教育的主要目标是产生良好的问题解决者，但是如何最好地教授和衡量解决问题的质量尚不清楚。该过程是复杂的，多方面的，且未完全表征。在这里，我们介绍了S＆E解决问题过程的理论框架，作为一组特定的相互联系的决策。该理论是基于基础的，并描述了整个过程。为了发展这一理论，我们采访了52名成功的科学家和工程师（专家），涵盖了包括生物学和医学在内的不同学科。他们描述了他们如何解决工作中典型但重要的问题，我们根据做出的决定分析了访谈。令人惊讶的是，我们发现在所有专家和领域中，解决方案过程都是围绕仅做出29个具体决定的。我们还发现，做出这些学科决策（在替代行动之间选择）的过程在很大程度上依赖于特定于领域的预测模型，这些模型体现了相关的学科知识。这组决策为S＆E解决问题的详细测量和教学提供了指南。该决策框架还提供了一个更具体，完整和经验的理论，描述了科学实践。
A primary goal of science and engineering (S & E) education is to produce good problem solvers, but how to best teach and measure the quality of problem-solving remains unclear. The process is complex, multifaceted, and not fully characterized. Here we present a theoretical framework of the S & E problem-solving process as a set of specific interlinked decisions. This theory is empirically grounded and describes the entire process. To develop this theory, we interviewed 52 successful scientists and engineers (experts) spanning different disciplines, including biology and medicine. They described how they solved a typical but important problem in their work, and we analyzed the interviews in terms of decisions made. Surprisingly, we found that across all experts and fields, the solution process was framed around making a set of just twenty-nine specific decisions. We also found that the process of making those discipline-general decisions (selecting between alternative actions) relied heavily on domain-specific predictive models that embodied the relevant disciplinary knowledge. This set of decisions provides a guide for the detailed measurement and teaching of S & E problem-solving. This decision framework also provides a more specific, complete, and empirically based theory describing the practices of science.
