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Research in K-12 Statistics Education

Applying Design-Based Research Findings to Improve the Common Core State Standards for Data and Statistics in Grades 4–6

References

  • Aridor, K., and Ben-Zvi, D. (2017), “The Co-Emergence of Aggregate and Modelling Reasoning,” Statistics Education Research Journal, 16, 38–63.
  • Bakker, A., and Gravemeijer, K. P. E. (2004), “Learning to Reason about Distribution,” in The Challenge of Developing Statistical Literacy, Reasoning, and Thinking, eds. D. Ben-Zvi and J. Garfield, Dordrecht, The Netherlands: Kluwer, pp. 147–168.
  • Bakker, A., and Hoffmann, M. (2005), “Diagrammatic Reasoning as the Basis for Developing Concepts: A Semiotic Analysis of Students’ Learning about Statistical Distribution,” Educational Studies in Mathematics, 60, 333–358. DOI: 10.1007/s10649-005-5536-8.
  • Bakker, A., and van Eerde, D. (2015), “An Introduction to Design-Based Research with an Example from Statistics Education,” in Doing Qualitative Research: Methodology and Methods in Mathematics Education, eds. A. Bikner-Ahsbahs, C. Knipping, and N. Presmeg, New York: Springer, pp. 429–466.
  • Cobb, P. (1999), “Individual and Collective Mathematical Development: The Case of Statistical Data Analysis,” Mathematical Thinking and Learning, 1, 5–43. DOI: 10.1207/s15327833mtl0101_1.
  • Cobb, P., Jackson, K., and Sharpe, C. (2017), “Conducting Design Studies to Investigate and Support Mathematics Students’ and Teachers’ Learning,” in Compendium for Research in Mathematics Education, ed. J. Cai, Reston, VA: National Council of Teachers of Mathematics, pp. 208–233.
  • Common Core State Standards Initiative (CCSSI) (2010), Common Core State Standards for Mathematics, Washington, DC: National Governors Association Center for Best Practices and the Council of Chief State School Officers.
  • Common Core State Standards Initiative (CCSSI) (2018), Standards in Your State, Washington, DC: National Governors Association Center for Best Practices and the Council of Chief State School Officers.
  • Frischemeier, D. (2018), “Design, Implementation, and Evaluation of an Instructional Sequence to Lead Primary School Students to Comparing Groups in Statistical Projects,” in Statistics in Early Childhood and Primary Education: Supporting Early Statistical and Probabilistic Thinking, eds. A. Leavy, M. Meletiou-Mavrotheris, and E. Paparistodemou, Singapore: Springer, pp. 217–238.
  • Garland, S. (2016), “In Texas, New Math Standards Look a Whole Lot Like Common Core,” The Hechinger Report, May 26.
  • Groth, R. E. (2017), “Developing Statistical Knowledge for Teaching During Design-Based Research,” Statistics Education Research Journal, 16, 376–396.
  • Groth, R. E. (2018), “Unpacking Implicit Disagreements among Early Childhood Standards for Statistics and Probability,” in Statistics in Early Childhood and Primary Education: Supporting Early Statistical and Probabilistic Thinking, eds. A. Leavy, M. Meletiou-Mavrotheris, and E. Paparistodemou, Singapore: Springer, pp. 149–162.
  • Groth, R. E., Kent, K., and Hitch, E. (2015), “Journey to Centers in the Core,” Mathematics Teaching in the Middle School, 21, 295–302.
  • Jacobs, V. R., and Spangler, D. A. (2017), “Research on Core Practices in K-12 Mathematics Teaching,” in Compendium for Research in Mathematics Education, ed. J. Cai, Reston, VA: National Council of Teachers of Mathematics, pp. 766–792.
  • Jones, G. A., Thornton, C. A., Langrall, C. W., Mooney, E. S., Perry, B., and Putt, I. J. (2000), “A Framework for Characterizing Children’s Statistical Thinking,” Mathematical Thinking and Learning, 2, 269–307. DOI: 10.1207/S15327833MTL0204_3.
  • Konold, C. (2002), “Teaching Concepts Rather Than Conventions,” New England Journal of Mathematics, 34, 69–81.
  • Konold, C., Higgins, T., Russell, S. J., and Khalil, K. (2015), “Data Seen through Different Lenses,” Educational Studies in Mathematics, 88, 305–325. DOI: 10.1007/s10649-013-9529-8.
  • Langrall, C., Nisbet, S., Mooney, E., and Jansem, S. (2011), “The Role of Context Expertise when Comparing Data,” Mathematical Thinking and Learning, 13, 47–67. DOI: 10.1080/10986065.2011.538620.
  • Loewus, L. H. (2015), “Nebraska’s New Math Standards: A Comparison to the Common Core,” Education Week, September 15.
  • Makar, K. (2018), “Theorising Links between Context and Structure to Introduce Powerful Statistical Ideas in the Early Years,” in Statistics in Early Childhood and Primary Education: Supporting Early Statistical and Probabilistic Thinking, eds. A. Leavy, M. Meletiou-Mavrotheris, and E. Paparistodemou, Singapore: Springer, pp. 3–20.
  • Masnick, A. M., Klahr, D., and Morris, B. J. (2007), “Separating Signal from Noise: Children’s Understanding of Error and Variability in Experimental Outcomes,” in Thinking with Data, eds. M. C. Lovett and P. Shah, Mahwah, NJ: Lawrence Erlbaum, pp. 3–26.
  • Matthews, M. E., Hlas, C. S., and Finken, T. M. (2009), “Using Lesson Study and Four-Column Lesson Planning with Preservice Teachers,” Mathematics Teacher, 102, 504–508.
  • Mokros, J., and Russell, S. J. (1995), “Children’s Concepts of Average and Representativeness,” Journal for Research in Mathematics Education, 26, 20–39. DOI: 10.2307/749226.
  • Mooney, E. S. (2002), “A Framework for Characterizing Middle School Students’ Statistical Thinking,” Mathematical Thinking and Learning, 4, 23–63. DOI: 10.1207/S15327833MTL0401_2.
  • National Council of Teachers of Mathematics (NCTM) (2006), Curriculum Focal Points for Prekindergarten through Grade 8 Mathematics: A Quest for Coherence, Reston, VA: NCTM.
  • Smith, J. P., ed. (2011), Variability is the Rule: A Companion Analysis of K-8 State Mathematics Standards, Charlotte, NC: Information Age.
  • Stein, M. K., Engle, R. A., Smith, M. S., and Hughes, E. K. (2008), “Orchestrating Productive Mathematical Discussions: Five Practices for Helping Teachers Move Beyond Show and Tell,” Mathematical Thinking and Learning, 10, 313–340. DOI: 10.1080/10986060802229675.
  • Wild, C. J., and Pfannkuch, M. (1999), “Statistical Thinking in Empirical Enquiry,” International Statistical Review, 67, 223–265. DOI: 10.1111/j.1751-5823.1999.tb00442.x.