Preparing students to meet their data: an evaluation of K-12 data science tools

Abstract

ABSTRACT Data science education has gained momentum in recent years. Along with the development of curricula to teach data science, the number and diversity of tools for introducing data science to learners are also multiplying. The tools used to teach data science play a central role in shaping the learning experience. Therefore, it is important to carefully choose which tools to use to introduce learners to data science. This article presents a systematic analysis of 30 data science tools that are, or designed to be, used in introductory data science education for K-12 students. The identified tools list includes spreadsheets, visual analysis tools, and scripting environments. For each tool, we examine facets of its capabilities, interactions, educational support, and accessibility. For block-based programming tools, we also examine the data science functionalities available in that tool’s blocks. This paper advances our understanding of the current state of introductory data science environments and highlights opportunities for creating new tools to better prepare learners to navigate the data-rich world surrounding them.