Notice: Presenters, titles, descriptions, dates, and times for the in-person schedule are subject to change as needed by the VSTE Conference committee.
The Virginia Department of Education has recently released new Data Science Standards of Learning for high school educators and learners, defining the skills and knowledge students might develop as they learn how to collect, analyze, interpret, and communicate about data. The development of these new standards affords teachers many new opportunities—data science is a fast-growing field with connections across many domains, from science to social studies. However, many educators and students may feel ill-prepared to implement these standards due to a lack of instructional resources, a dearth of information about the contours of high school data science instruction, and few opportunities to extend their prior knowledge with new data science content knowledge and pedagogical skills.
In this session, participants will explore how they might facilitate data science learning experiences in three paradigms with different levels of accessibility and rigor: “unplugged” data science, where students engage in collecting, analyzing, and modeling data without computational tools; “plugged” data science with CODAP, a no-code data analysis and visualization tool that runs in a web browser; and “plugged” data science with the Python coding language. Participants will engage in hands-on activities in one or more of these paradigms and discuss the affordances and constraints of “unplugged” and “plugged” data science, while imagining how they might empower students to develop personalized, interest-driven data science projects. In addition to exploring these data science paradigms, participants will receive access to around 30 free “plugged” and “unplugged” data science lesson plans and project-based learning resources developed by CodeVA that they can implement with their students. Throughout the session, participants will explore the following questions: What is data science, and what are the skills and knowledge students should develop to meet the Virginia Data Science SOLs? What tools might students use to practice data science? How might I empower students to develop interest-driven data science projects that connect to their individual goals, needs, and desires as scholars and learners?
