Data Detectives: A Data Science Program for Middle Grade Learners

References

• Baumer, B. (2015), “A Data Science Course for Undergraduates: Thinking With Data,” The American Statistician, 69, 334–342. DOI: https://doi.org/10.1080/00031305.2015.1081105.
   Web of Science ®Google Scholar
• Cleveland, W. S. (2014), “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics,” Statistical Analysis and Data Mining: The ASA Data Science Journal, 7, 414–417. DOI: https://doi.org/10.1002/sam.11239.
   Web of Science ®Google Scholar
• Datta, S., and Nagabandi, V. (2017), “Integrating Data Science and R Programming at an Early Stage,” In 2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence(ISCMI).
   Google Scholar
• Dryer, A., Walia, N., and Chattopadhyay, A. (2018), “A Middle-School Module for Introducing Data-Mining, Big-Data, Ethics and Privacy Using RapidMiner and a Hollywood Theme,” in Proceedings of the 49th ACM Technical Symposium on Computer Science Education – SIGCSE 18. DOI: https://doi.org/10.1145/3159450.3159553.
   Google Scholar
• Franklin, C., Kader, G., Mewborn, D., Moreno, J., Peck, R., Perry, M., and Scheaffer, R. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report.
   Google Scholar
• Gould, R., Machado, S., Ong, C., Johnson, T., Molyneux, J., Nolen, S., Tangmunarunkit, H., Trusela, L., and Zanontian, L. (2016), “Teaching Data Science to Secondary Students the Mobilize Introduction to Data Science Curriculum,” in Promoting Understanding of Statistics about Society, ed. J. Engel, Proceedings of the IASE Roundtable Conference.
   Google Scholar
• Grolemund, G., and Wickham, H. (2016), “R For Data Science. OReilly Online Learning,” Available at https://www.oreilly.com/library/view/r-for-data/9781491910382/
   Google Scholar
• Hardin, J., Hoerl, R., Horton, N. J., Nolan, D., Baumer, B., Hall-Holt, O., Murrell, P., Peng, R., Roback, P., Temple Lang, D., and Ward, M. D. (2015), “Data Science in Statistics Curricula: Preparing Students to “Think with Data,” The American Statistician, 69, 343–353. DOI: https://doi.org/10.1080/00031305.2015.1077729.
   Web of Science ®Google Scholar
• Harel, I., and Papert, S. (1991), Constructionism, Norwood, NJ: Ablex Pub. Corp.
   Google Scholar
• Heinemann, B. (2018), “Drafting a Data Science Curriculum for Secondary Schools,” in Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling 18. DOI: https://doi.org/10.1145/3279720.3279737.
   Google Scholar
• Jiang, S., and Kahn, J. (2019), “Data Wrangling Practices and Process in Modeling Family Migration Narratives with Big Data Visualization Technologies,” in International Conference on Computer Supported Collaborative Learning.
   Google Scholar
• Kafai, Y. B. (1995), Minds in Play: Computer Game Design as a Context for Children’s Learning, Mahwah, NJ: Erlbaum.
   Google Scholar
• Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., Willing, C., and The Jupyter Development Team (2016), “Jupyter Notebooks—A Publishing Format for Reproducible Computational Workflows,” in Positioning and Power in Academic Publishing: players, Agents and Agendas: proceedings of the 20th International Conference on Electronic Publishing, p. 87. Amsterdam: IOS Press.
   Google Scholar
• Lazar, N., and Franklin, C. (2015), “The Big Picture: Preparing Students for a Data-centric World,” CHANCE, 28, 43–45. DOI: https://doi.org/10.1080/09332480.2015.1120128.
   Google Scholar
• Lee, V. R., and Wilkerson, M. (2018), “Data use by Middle and Secondary Students in the Digital Age: A Status Report and Future Prospects,” Commissioned Paper for the National Academies of Sciences, Engineering, and Medicine, Board on Science Education, Committee on Science Investigations and Engineering Design for Grades 6–12. Washington, D.C.
   Google Scholar
• National Council of Teachers of Mathematics (NCTM). (2000), Principles and Standards for School Mathematics, Reston, VA: NCTM.
   Google Scholar
• Papert, S. (1980), Mindstorms. Children, Computers and Powerful Ideas, New York: Basic Books.
   Google Scholar
• Quintana, C., Eng, J., Carra, A., Wu, H., and Soloway, E. (1999), “Symphony: A Case Study in Extending Learner-Centered Design Through Process-Space Analysis,” In Human Factors in Computing Systems: CHI ’99 Conference Proceedings, Pittsburgh, PA: ACM Press.
   Google Scholar
• Satyanarayana, A. (2013), “Software Tools for Teaching Undergraduate Data Mining Course,” in American Society of Engineering Education Mid- Atlantic Fall Conference.
   Google Scholar
• Srikant, S., and Aggarwal, V. (2017), “Introducing Data Science to School Kids,” in Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education - SIGCSE 17.
   Google Scholar
• Statistical Thinking: The Bedrock of Data Science. (2013), HuffPost. Available at www.huffpost.com/entry/statistical-thinking-the-bedrock-of-data-science_b_3651121.
   Google Scholar
• Stephenson, C., Cooper, S., Owens, B. B., and Gal-Ezer, J. (2012), “The New CSTA K–12 Computer Science Standards,” in Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education - ITiCSE 12.
   Google Scholar
• Thompson, J., and Wu, S. P. (2020), “The Use of Computer Programming in a Secondary Mathematics Class,” in Proceedings of ASEE Annual Conference.
   Google Scholar
• Wilensky, U. (2003), “Statistical Mechanics for Secondary School: The GasLab Multi-Agent Modeling Toolkit,” International Journal of Computers for Mathematical Learning, 8, 1–41. DOI: https://doi.org/10.1023/A:1025651502936.
   Google Scholar