TY - GEN
T1 - Preparing K-12 Students to Meet their Data
T2 - 2023 Symposium on Learning, Design and Technology, LDT 2023
AU - Israel-Fishelson, Rotem
AU - Moon, Peter F.
AU - Tabak, Rachel
AU - Weintrop, David
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s).
PY - 2023/6/23
Y1 - 2023/6/23
N2 - 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 review of 25 data science tools that are, or can 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. 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.
AB - 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 review of 25 data science tools that are, or can 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. 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.
KW - Data analysis tools
KW - Data science education
KW - Introductory data science
UR - https://www.scopus.com/pages/publications/85163735277
U2 - 10.1145/3594781.3594796
DO - 10.1145/3594781.3594796
M3 - Conference contribution
AN - SCOPUS:85163735277
T3 - ACM International Conference Proceeding Series
SP - 29
EP - 42
BT - Proceedings of 2023 Symposium on Learning, Design and Technology, LDT 2023
PB - Association for Computing Machinery
Y2 - 23 June 2023
ER -