This 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 LogicDataScience (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.

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

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.

## Project Materials

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