The demand for high-quality, engaging, and relatable datasets is soaring as the demand for K-12 data science — and its close cousins, machine learning (ML) and artificial intelligence (AI)—grows. Indeed, early research shows that a student’s choice of dataset has a substantial impact on their engagement!
There are already many datasets freely available online, or ready to be provided by industry partners. Unfortunately, using these datasets in classrooms or curricula often requires additional work to take a dataset designed for a specific purpose and audience and adapt it to be accessible and relevant to a K-12 student. This work can be a barrier for educators.
Data Science for Everyone (DS4E)
coalition members Emmanuel Schanzer (Bootstrap) and Dan Schneider (Code.org), have developed an in depth specification guide that offers a pathway for individuals to find, clean, document, and upload datasets that can be used in K-12 data science tools.
Read the full blog and learn more about these efforts here.
You can find the complete guide here.