Purpose

Data science, artificial intelligence, machine-learning, and related disciplines are rapidly evolving technology fields that carry significant implications for our students, regardless of their career choice. More importantly, navigating daily life in the 21st century now entails several encounters with data, both in personal and professional settings. Creating equal access to data literacy education will be a critical challenge for our economy, our workforce, and our democracy.

IES is committed to supporting the use of evidence-based practices in the design and implementation of educational programs in these areas—while acknowledging that, as a field, we continue to learn together what works best, for whom, and under what conditions.

Intended for PK–12 leaders and educators, this site provides a checklist for guiding design, implementation, and evidence gathering in new programs for data science education and related fields. The site also catalogs a variety of resources, including those created outside of IES, that may be useful references for first implementing data science and data literacy education programming. Keep in mind that while some practices are based on rigorous evidence, many are rooted in theory but have yet to be tested. While some steps may seem obvious, even the most practiced experts benefit from checklists and reminders.

As we use and share today's best available evidence on data science and data literacy learning, we must continue to build stronger, more rigorous evidence about "what works" going forward. In doing so, educators can begin to address historic inequities in STEM Education and exacerbated by COVID-19, including those affecting students of color, students from low-income backgrounds, students with disabilities, English learners, students who are migratory, students experiencing homelessness, students in correctional facilities, and students in foster care.