Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop (2023)
Chapter: References
References
Amrhein, V., Trafimow, D., and Greenland, S. (2019). Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(Supp1), 262–270. https://doi.org/10.1080/00031305.2018.1543137
Lee, C., and Soep, E. (2023). Code for What? MIT Press. https://mitpress.mit.edu/9780262047456/code-for-what/
National Academies of Sciences, Engineering, and Medicine. (2021). Cultivating Interest and Competencies in Computing: Authentic Experiences and Design Factors. The National Academies Press. https://doi.org/10.17226/25912
———. (2018). Data Science for Undergraduates: Opportunities and Options. The National Academies Press. https://doi.org/10.17226/25104
National Academy of Engineering and National Research Council. (2014). STEM Integration in K–12 Education: Status, Prospects, and an Agenda for Research. The National Academies Press. https://doi.org/10.17226/18612
National Research Council. (2012). A Framework for K–12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. The National Academies Press. https://doi.org/10.17226/13165
Podworny, S., Fleischer, Y., Hüsing, S., Biehler, R., Frischemeier, D., Höper, L., and Schulte, C. (2021). Using data cards for teaching data based decision trees in middle school. 21st Koli Calling International Conference on Computing Education Research (Koli Calling ‘21), November 18–21, 2021, Joensuu, Finland. Association for Computing Machinery. https://doi.org/10.1145/3488042.3489966
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