Integrated Data Science for Secondary Schools: Design and Assessment of a Curriculum
Integrated Data Science for Secondary Schools: Design and Assessment of a Curriculum
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中学综合数据科学：课程设计和评估
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
S. Krishnamurthi
中科院分区：
文献类型：
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作者：
Emmanuel Schanzer;Nancy Pfenning;Flannery Denny;Sam Dooman;J. Politz;Benjamin S. Lerner;Kathi Fisler;S. Krishnamurthi
我们建议中学数据科学课程应该基于四个关键要素：两个是技术性的（编程和统计，可视化位于它们的交叉点），而两个是面向人类的（有意义的领域和公民责任）。我们描述了它们的关系，并论证了它们的重要性。在此基础上，我们提出了Bootstrap：数据科学课程，旨在整合到多个学科和设置。它通过（a）设计为一组可混合的课程，（B）让班级和学生选择个人有意义的数据集来实现这一点。我们还启动了评估这一课程的过程。我们创建了两个评估工具，一个侧重于学习，另一个侧重于个性化和参与。我们提供了从学生和教师那里收集的非常初步的数据。
We propose that secondary-school data-science curricula should be based on four key ingredients: two are technical (programming and statistics, with visualization sitting at their intersection), while two are human-facing (meaningful domains, and civic responsibility). We describe their relationship and argue for their importance. Based on this, we then present the Bootstrap:Data Science curriculum, designed for integration into multiple disciplines and settings. It achieves this by (a) being designed as a set of remix-able lessons, and (b) letting classes and students choose personally meaningful datasets. We also initiate the process of evaluating this curriculum. We create two assessment instruments, one focused on learning and the other on personalization and engagement. We provide very preliminary data gathered from students and teachers.
错误消息是分类器：设计和评估错误消息的过程
DOI：
10.1145/3133850.3133862
发表时间：
2017
期刊：
and Reflections on Programming and Software
影响因子：
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作者：
Wrenn, John;Krishnamurthi, Shriram
通讯作者：
Krishnamurthi, Shriram