For decades, introductory statistics has been taught in universities and high schools with traditional approaches that emphasize techniques and tools over practical applications.
A summer workshop headed by the University of Wyoming’s Department of Statistics and the National Center for Atmospheric Research (NCAR) will emphasize data analysis that allows users to explain how the data tell a story that is relevant to solving real-world problems.
UW and NCAR will host a workshop titled “Data Analytics for the Geosciences Using R” at NCAR’s Mesa Lab in Boulder, Colo., June 16-19. The workshop is geared toward math and statistics teachers who use data analysis in their courses. Attendee preference will be given to faculty at community colleges who teach introductory statistics and high school teachers who teach AP statistics in Wyoming and along the Front Range; faculty at historically black colleges and universities; and faculty at minority-serving institutions. The workshop is limited to 20 people.
“We need to shift the paradigm of how we teach statistics,” says Tim Robinson, a professor in UW’s Department of Statistics, who will co-lead the workshop with Doug Nychka, director of NCAR’s Mathematics Applied to Geosciences (IMAGe). “NCAR recognizes that. We have to start with the foundation. Instead of using sanitized examples in textbooks, let’s provide real data.”
Robinson’s sense of urgency for change stems from two fronts. First, he points to a 2011 Harvard Study on Educational Policy and Governance titled “Globally Challenged: Are U.S. Students Ready to Compete?,” which ranks the United States 32nd among countries worldwide in mathematical proficiency. Second, Robinson referenced the National Governors Association Center for Best Practices’ effort to create a Common Core State Standards Initiative for K-12 education that would better prepare students for college and the workplace. Forty-five states, including Wyoming, have adopted these standards, he says.
“We have adopted standards and now teachers, who were trained traditionally, are scrambling with not only learning these skills, but also how to teach and bring (these methods) to real-life examples,” Robinson says.
Give me an “R”
The workshop will teach participants how to use “R” in a hands-on and tutorial environment, and explore substantial data sets in a way that will motivate a variety of statistical methods. R is a publicly available software package that was developed by the international statistics community and is a standard for current statistical methodology in industry, government and academia. Worldwide, there are more than 2 million users of “R.” Boeing, Google and Proctor & Gamble are some major corporations that widely use the “R” software, Robinson says.
Geosciences at NCAR focus on the behavior of the atmosphere and related physical, biological and social systems. Although the data sets during the workshop will largely be drawn from the environmental and geosciences, participants will be encouraged to consider data from other fields to match their interests.
“We will look at data sets involving climate change, habitat selection by wolves, sugar beet crop yields, and other data relevant to Wyoming and Colorado communities,” Robinson continues. “All of the problems have multiple, competing solutions, and all of the data and scenarios will be available to be taken back to home campuses for use in classrooms in those institutions.”
While the workshop’s focus will be on integrating a new teaching approach in classrooms, statistics are something anyone can use in their everyday lives, Robinson stresses. Some examples include calculating the cost of living in different geographic areas; researching the best facilities to receive health care treatment; and even comparing hotel prices when planning a vacation. Today, analyzing data is not relegated to statisticians but, rather, we all need to be proficient in data analysis, Robinson says.
Robinson offered a number of analogies to describe the difference between the current method of teaching statistics and this workshop’s attempt to move statistics education to something more relevant. For example, in times past, a wildlife biologist who studied mule deer populations would have focused mainly on field work. Modeling and producing physical maps depicting mule deer habitat preferences would have been left to other specialists. Today, it is imperative that the wildlife biologist possess all of those skills, he says.