Collaborative Research: Fostering Virtual Learning of Data Science Foundations with Mathematical Logic for Rural High School Students

数据科学正在彻底改变科学和工业，当前的就业市场对精通数据科学的劳动力需求强劲。佛罗里达虚拟学校（FLVS）的LogicDataScience（LogicDS）课程正在向高中生介绍这一令人兴奋的领域和相关职业。该项目解决了一个主要的教育障碍，即农村学生不太可能选择STEM专业，也很少有机会获得由高素质教师教授的STEM高级课程。LogicDS课程和虚拟交付预计将大大缓解资源限制，从而惠及农村学生。数据科学课程开发背后的策略探索了强调数据科学在计算，数学和统计学中的基础如何通过数学逻辑统一起来的实用性。该项目正在研究新课程对学生学习计算、数学和统计的影响。拟议课程的核心是一种原则性，统一性和新颖性的方法，通过数学逻辑自然而严格地整合了计算，数学和统计学中的数据科学基础。计算，数学和统计学的基础是相互联系的，并在现实生活中，在这个课程中的数据丰富的问题。这种方法做了两件事，为数据科学教育带来了根本的希望。首先，它以有助于数据科学学习者的方式绘制了统计和计算之间的关系。其次，它减少了认知负荷，使数据科学的关键结构的学习更容易获得。一个基于设计的研究范式指导该项目，这是直接影响约1,000名学生。该项目使用微观分析来研究课程理念所涵盖的学生学习机制。此外，本研究还采用准实验研究的方法，比较研究了新课程对学生学习计算、数学和统计的影响，以及新课程对学生学习的影响方式。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，显着提高preK-12学生和教师对STEM科目的学习和教学。DRK-12项目中的项目建立在STEM教育的基础研究以及为拟议项目提供理论和经验依据的先前研究和开发工作的基础上。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Data science is revolutionizing science and industry, and the current job market has shown a strong demand for a workforce fluent in data science. The LogicDataScience (LogicDS) curriculum in the Florida Virtual Schools (FLVS) is introducing high school students to this exciting area and related careers. The project addresses a major educational barrier, namely that rural students are less likely to choose a major in STEM and have far less access to advanced STEM courses taught by highly qualified teachers. The LogicDS curriculum and virtual delivery are expected to relieve the resource constraints significantly and thus reach rural students. The strategy behind this curriculum development for data science explores the utility of emphasizing how the foundations of data science in computing, mathematics, and statistics are unified by mathematical logic. The project is studying the impacts of the new curriculum on students’ learning of computing, mathematics, and statistics. At the core of the proposed curriculum is a principled, unified, and novel approach naturally and rigorously integrating the foundations of data science in computing, mathematics, and statistics through mathematical logic. The foundations of computing, mathematics, and statistics are contextualized in each other and in real-life, data-rich problems in this curriculum. This approach does two things that hold fundamental promise for data science education. First, it draws relations between statistics and computing in ways that are helpful for data science learners. Second, it reduces cognitive load and makes learning of key constructs of data science more accessible. A design-based research paradigm guides the project, which is directly impacting about 1,000 students. The project uses microgenetic analysis to study the mechanisms underlying student learning premised by the rationale of the curriculum. In addition, a quasi-experimental study is being employed to investigate comparatively the impact of the new curriculum on students’ learning of computing, mathematics, and statistics and in what way it affects their learning. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of STEM subjects 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.