Learning Progressions in Context: Tensions and Insights from a Semester-Long Middle School Modeling Curriculum.
Learning Progressions in Context: Tensions and Insights from a Semester-Long Middle School Modeling Curriculum.
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背景下的学习进展：一学期中学建模课程的张力和见解。
DOI：
10.1002/sce.21314
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发表时间：
2017
影响因子：
4.3
通讯作者：
Maximilian Sherard
中科院分区：
文献类型：
--
作者：
Ashlyn E. Pierson;Douglas B. Clark;Maximilian Sherard
施瓦茨和同事们提出并完善了建模的学习过程，为在聚合水平上设想日益复杂的建模实践水平提供了有价值的模板（Fortus，Shwartz，& Rosenfeld，2016;施瓦茨等人，2009;施瓦茨，雷泽，Archer，Kenyon，& Fortus，2012）.然而，考虑建模的学习进展涉及到课程中的聚合弧与单个学生学习轨迹之间的协调挑战。首先，学生个人的表现往往取决于学生的认知目标和概念和表征背景的性质。其次，纵向支持学生建模的方法是一个相对新生的奋进，尽管已经开发了值得注意的范例（例如，IQWST）。第三，对所提出的进展的最高水平的研究通常是假设的，因为很少有学生在典型的课堂上展示高水平的建模实践。为了应对这些挑战，我们进行了为期一个学期的设计为基础的研究八年级学生从事图表，物理和计算建模。在本文中，我们探讨的概念和代表性的背景下，旨在支持复杂的建模实践和信念，分析通过这些背景下实现的高层次的性能的性质，并建议修改的衔接施瓦茨和同事的学习进展，以增加其效用和概括性时，通过资源相关的透镜。
Schwarz and colleagues have proposed and refined a learning progression for modeling that provides a valuable template for envisioning increasingly sophisticated levels of modeling practice at an aggregate level (Fortus, Shwartz, & Rosenfeld, 2016; Schwarz et al., 2009; Schwarz, Reiser, Archer, Kenyon, & Fortus, 2012). Thinking about learning progressions for modeling, however, involves challenges in coordinating between aggregate arcs in the curriculum and individual student learning trajectories. First, individual student performance is often dependent on students’ epistemic aims and the nature of the conceptual and representational context. Second, approaches for longitudinally supporting students in modeling is a relatively nascent endeavor, although notable exemplars have been developed (e.g., IQWST). Third, research on the highest levels of the proposed progression is often hypothetical, because few students demonstrate high-level modeling practices in typical classrooms. In response to these challenges, we conducted a semester-long design-based study of eighth graders engaging in diagrammatic, physical, and computational modeling. In this paper, we explore conceptual and representational contexts designed to support sophisticated modeling practices and beliefs, analyze the nature of high-level performances achieved through these contexts, and suggest revisions to the articulation of the Schwarz and colleagues learning progression to increase its utility and generalizability when viewed through a resource-related lens.