CodeR4STATS - Code R for AP Statistics

探索研究K-12计划(DRK-12)旨在通过研究和开发创新资源、模型和工具，显著提高K-12学龄前学生和教师在科学、技术、工程和数学(STEM)方面的学习和教学。所有STEM领域越来越依赖于能够理解数据和使用统计数据。这个项目建立在先前的努力基础上，为高中高级就业统计教师创建教学资源，以便在他们的课堂上使用一种名为“R”的开放源码统计编程语言。该项目汇集了来自不同STEM领域的数据集，并将制定练习和评估，以教会学生如何用R编程并学习基本的统计概念。因此，本项目试图帮助学生在AP统计的背景下同时学习编码、统计和STEM。此外，研究人员将检查学生在多大程度上更好地利用在线工具学习统计概念、计算流畅性和关键推理技能。该项目开发的资源旨在通过综合应用以前记载有效的战略来加强统计学学习：侧重于数据可视化和表示，让学生参与对复杂的现实世界数据集的有意义的调查，利用计算工具和技术分析数据，并使教育工作者更好地为更复杂和技术丰富的数学图景的需求做好准备。该项目将把这些工作线结合到一个名为CodeR4STATS的精简教学环境中，该环境具有三种资源：计算资源、数据集和评估资源。计算资源将包括免费访问基于云的R-Studio实例，并提供定制帮助页面。数据资源将包括来自伍兹霍尔海洋研究所、哈佛大学定量社会科学研究所、哈伯德·布鲁克实验森林、波士顿大学和塔夫茨大学的800多个科学数据集，其中有几个突出显示在针对学生的案例研究中；这些数据集将在在线环境中进行搜索。评估和辅导资源将使用辅导平台ASSISTments提供，该平台使用范例追踪来提供评估、反馈和量身定制的指导。还将提供教师培训和教师在线讨论板。将这些资源汇集在一起的将是规划实验室活动、五个真实世界的案例研究，以及与共同核心数学标准相关的16个统计作业。研究人员将在两年内使用来自三个教室的课堂观察性案例研究，包括计算环境中的课程与离线课程的跨案例比较；学生和教师访谈；以及对来自在线系统的学习者数据的分析，特别是基于ASSISTments的评估数据。这项研究将检验学习结果，并帮助完善统计学习环境的设计原则。

The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Increasingly, all STEM fields rely on being able to understand data and use statistics. This project builds on prior efforts to create teaching resources for high-school Advanced Placement Statistics teachers to use an open source statistics programming language called "R" in their classrooms. The project brings together datasets from a variety of STEM domains, and will develop exercises and assessments to teach students how to program in R and learn the underlying statistics concepts. Thus, this project attempts to help students learn coding, statistics, and STEM simultaneously in the context of AP Stats. In addition, researchers will examine the extent to which students learn statistical concepts, computational fluency, and critical reasoning skills better with the online tools. The resources developed by the project aim to enhance statistics learning through an integrated application of strategies previously documented to be effective: a focus on data visualization and representation, engaging students in meaningful investigations with complex real-world data sets, utilizing computational tools and techniques to analyze data, and better preparing educators for the needs of a more complex and technologically-rich mathematical landscape. This project will unite these lines of work into one streamlined pedagogical environment called CodeR4STATS with three kinds of resources: computing resources, datasets, and assessment resources. Computing resources will include freely available access to an instance of the cloud-based R-studio with custom help pages. Data resources will include over 800 scientific datasets from Woods Hole Oceanographic Institute, Harvard University's Institute for Quantitative Social Science, Hubbard Brook Experimental Forest, Boston University, and Tufts University with several highlighted in case studies for students; these will be searchable within the online environment. Assessment and tutoring resources will be provided using the tutoring platform ASSISTments which uses example tracing to provide assessment, feedback, and tailored instruction. Teacher training and a teacher online discussion board will also be provided. Bringing these resources together will be programming lab activities, five real-world case studies, and sixteen statistics assignments linked to common core math standards. Researchers will use classroom observational case studies from three classrooms over two years, including cross-case comparison of lessons in the computational environment versus offline lessons; student and teacher interviews; and an analysis of learner data from the online system, especially the ASSISTments-based assessment data. This research will examine learning outcomes and help refine design principles for statistics learning environments.