Improving Statistics within K-12 Mathematics
by Megan Parise Schmidt
University of Minnesota, Twin Cities
In 2000, the National Council of Teachers of Mathematics officially positioned statistics as an integral part of a well-rounded mathematics curriculum by including a “Data Analysis and Probability” strand in the monumental document Principles and Standards for School Mathematics (NCTM, 2000). Since the merging of these two disciplines, K-12 educators have been challenged with the teaching and learning of statistics under the school mathematics umbrella. More recently, the American Statistical Society has stepped in to provide a Guidelines for Assessment and Instruction in Statistics Education report in which they provide a curriculum framework to assist mathematics teachers in this work (Franklin et al, 2005). However, K-12 mathematics educators continue to struggle with effectively teaching statistical concepts.
One reason for this persistent difficulty is simply lack of training. Most secondary mathematics teacher education programs do not require a robust sequence of statistics courses nor do they include an instructional methods course dedicated to statistics-focused pedagogical content knowledge. Secondly, the misguided understanding of statistics as a sub-discipline of mathematics can lead to instructional strategies that hinder a conceptual understanding of key ideas like variability and sampling.
Yet, a rapidly growing number of college majors are now requiring a course in statistics rather than in calculus. Therefore, a solid grounding in data analysis and probability built in the elementary and secondary grades does more than merely prepare students for high stakes tests like the MCA’s. And the applicability of statistics may provide a much more satisfying answer to the age-old math class question, “when are we ever going to use this?”
Given that the development of statistical literacy is in our students’ best interests (as well as our own as educators), what can we do in order to improve the quality of our statistics instruction? In a professional development course of secondary mathematics teachers at the University of Minnesota, Garfield and Ben-Zvi (2008) use the following six principles of statistics instructional design developed by Cobb and McClain (2004):
- Focus on developing central statistical ideas rather than on presenting a set of tools and procedures.
- Use real and motivating data sets to engage students in making and testing conjectures.
- Use classroom activities to support the development of students’ reasoning.
- Integrate the use of appropriate technological tools that allow students to test their conjectures, explore and analyze data, and develop their statistical reasoning.
- Promote classroom discourse that includes statistical arguments and sustained exchanges that focus on significant statistical ideas.
- Use assessment to learn what students know and to monitor the development of their statistical learning as well as to evaluate instructional plans and progress.
(Garfield & Ben-Zvi, 2008)
These principles have a similar feel to those outlined in NCTM’s Principles and Standards for School Mathematics. However, the two diverge within statistics principle two, using real and motivating data to engage students. While exploring the ways mathematics teachers define “real” and “motivating” are beyond the scope of this article, this principle does seem to suggest these types of datasets go beyond the ones usually provided by statistics textbooks.
As a doctoral student at the University of Minnesota, I have unexpectedly found myself immersed in scholarship related to the teaching of statistics in the K-12 mathematics classroom. Although this particular contribution to Math Bits is brief, I want to continue this conversation in the months to come in order to help improve statistics education within the K-12 classroom so that Minnesota students can be better equipped with the statistical literacy they need to thrive in this data-driven world.
(References available upon request)