Boosting Data Science Teaching and Learning in STEM

数据流畅性是在数据世界中导航的能力。这包括了解数据来源、为分析构建数据结构、解释数据表示法、从数据推断含义以及向不同受众解释数据和调查结果。数据科学作为一种职业机会和解决STEM学科中复杂现象的机制正变得越来越重要。这个项目解决了帮助中学教师学习将数据科学融入他们的教学中的迫切需要。它使用一个名为通用在线数据分析平台(CODAP)的开源平台作为教师学习数据科学和开发学生学习资源的工具。我们将开发一个教师数据科学教与学知识的框架。来自该项目的见解将有助于开发有效的数据科学教学实践和了解学生如何学习数据科学。该项目将产生两个关键产品：教师数据流畅性框架和一套教师数据科学专业学习资源，包括说明数据科学教与学进展的课堂实践案例和表面常见的学生障碍，现场专业学习社区的材料，以及专业学习模块，使教师参与科学教育标准和STEM教育更广泛地呼吁的那种数据丰富的学习。该项目将包括两个阶段。在第一阶段，该项目将使用基于设计的研究方法来开发一个中学数据流畅性的教学内容知识模型。第一阶段将回答以下问题：(1A)教师需要知道和能够做什么来支持学生变得数据流畅？(1B)学生在提高数据流畅性方面常见的学生误解和障碍是什么？(1C)提高教师数据流畅性和支持学生数据流畅性的专业学习的核心组成部分是什么？在第二阶段，该项目将使用混合方法方法来研究模型的实施。第二阶段将解决以下问题：(2A)第一阶段中确定的核心组成部分的专业学习对教师提供给学生的学习机会和学生的数据流畅性有什么影响？(2B)专业学习创新对最终用户有用和可行吗？(2C)教师和学生的课堂互动以什么方式反映了第一阶段发展的教学内容知识模型？关于教师需要什么知识和技能来支持学生的数据流畅性，有什么证据支持或驳斥这一假设？探索研究PREK-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Pre-K-12学生和教师在科学、技术、工程和数学(STEM)方面的学习和教学。DRK-12计划中的项目建立在STEM教育的基础研究和先前的研究和开发工作的基础上，为拟议的项目提供了理论和经验上的证明。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Data fluency is the ability to navigate the world of data. This includes understanding the sources of data, structuring data for analysis, interpreting representations of data, inferring meaning from data, and explaining data and findings to diverse audiences. Data science is becoming more important as a career opportunity and a mechanism for addressing complex phenomena in STEM disciplines. This project addresses a critical need to help middle school teachers learn to incorporate data science in their teaching. It uses an open-source platform called the Common Online Data Analysis Platform (CODAP) as a tool for teachers to learn about data science and develop resources for students’ learning. We will develop a framework for teachers’ knowledge of data science teaching and learning. Insights from the project will help develop effective practices for teaching data science and understanding how students learn data science.This project will result in two key products: a framework for teacher data fluency and a set of resources for teacher professional learning in data science, including cases of classroom practice that illustrate teaching and learning progressions in data science and surface common student roadblocks, materials for site-based Professional Learning Communities, and professional learning modules that engage teachers in the kind of data-rich learning called for by science education standards and STEM education more broadly. The project will include two stages. During stage one, the project will use a design-based research approach to develop a model of pedagogical content knowledge for data fluency in middle school. Stage one will answer the following questions: (1a) What do teachers need to know and be able to do to support students in becoming data fluent? (1b) What are common student misconceptions and roadblocks in students’ progress to data fluency? (1c) What are the core components of professional learning that boost teachers’ data fluency and their ability to support students becoming data fluent? During stage two, the project will use a mixed methods approach to study the model’s implementation. Stage two will address the following questions: (2a) What impact does professional learning with the core components identified in stage one have on the opportunities to learn teachers provide to their students and on their students’ data fluency? (2b) Are the professional learning innovations usable and feasible for the end users? (2c) In what ways do teachers’ and students’ classroom interactions reflect the model of pedagogical content knowledge developed in stage one? What evidence supports or refutes the hypothesis about the knowledge and skills teachers need to support students’ movement to data fluency? The Discovery Research preK-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. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.