Principled Assessment of Student Learning in High School Computer Science
Principled Assessment of Student Learning in High School Computer Science
复制标题
高中计算机科学学生学习的原则评估
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Yuning Xu
中科院分区：
文献类型：
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作者：
E. Snow;Daisy W. Rutstein;M. Bienkowski;Yuning Xu
随着K-12计算机科学（CS）计划在美国的规模，教育工作者面临着来自学校系统的越来越大的压力，要求他们提供关于学生学习难以衡量的CS结果的证据。与此同时，研究课程实施和学生学习的研究人员希望可靠的措施，学生如何应用他们的CS知识。本文介绍了一个为期两年的验证研究，侧重于探索计算机科学，介绍高中CS课程的单元结束和累积评估。为了开发评估，我们应用了一种名为以证据为中心的设计（ECD）的原则性方法来（1）与各种利益相关者合作，以确定要测量的重要计算机科学技能，（2）将这些技能映射到可以支持对这些技能的推断的证据模型，以及（3）开发评估任务，以引出这些证据。使用ECD，我们创建了测量计算思维实践的评估，与仅测量CS概念知识的评估相反。我们迭代开发和试点的评估与941名学生超过两年，并收集了三种类型的有效性证据的基础上当代心理测量标准：测试内容，内部结构和学生的反应过程。结果表明，每个单元评估的可靠性都是中等偏高的;每个评估中的评估任务彼此之间以及与有针对性的学习目标保持一致;平均得分在60%至70%的范围内。这些结果表明，评估有效地衡量学生的计算思维的做法涵盖在介绍CS课程。我们讨论了更广泛的问题，我们面临的平衡需要使用评估结果的评价和研究，并要求教师在课堂上使用。
As K-12 computer science (CS) initiatives scale throughout the U.S., educators face increasing pressure from their school systems to provide evidence about student learning on hard-to-measure CS outcomes. At the same time, researchers studying curriculum implementation and student learning want reliable measures of how students apply their CS knowledge. This paper describes a two-year validation study focused on end-of-unit and cumulative assessments for Exploring Computer Science, an introductory high school CS curriculum. To develop the assessments, we applied a principled methodology called Evidence-Centered Design (ECD) to (1) work with various stakeholders to identify the important computer science skills to measure, (2) map those skills to a model of evidence that can support inferences about those skills, and (3) develop assessment tasks that elicit that evidence. Using ECD, we created assessments that measure the practices of computational thinking, in contrast to assessments that only measure CS conceptual knowledge. We iteratively developed and piloted the assessments with 941 students over two years and collected three types of validity evidence based on contemporary psychometric standards: test content, internal structure, and student response processes. Results show that reliability was moderate to high for each of the unit assessments; the assessment tasks within each assessment are well aligned with each other and with the targeted learning goals; and average scores were in the 60 to 70 percent range. These results indicate that the assessments validly measure students' computational thinking practices covered in the introductory CS curriculum. We discuss the broader issues we faced of balancing the need to use the assessment results for evaluation and research, and demands from teachers for use in the classroom.