As they point out, a wide range of scientific fields rely on statistical methods to make research conclusions, and understanding the basic logic of these methods is critical for citizens’ science literacy. Starns explains that this project seeks to revamp statistics education by developing methods that promote deep conceptual understanding. “We are teaching people to understand math equations by first teaching them a visual way to find the answer then showing them how to link the visual method to each step in the math equation,” he notes.
Helping students understand basic probability concepts is critical to promoting science literacy, as well as to equipping STEM majors with the skills and knowledge they need to succeed in graduate school, the workplace and beyond, Starns and colleagues point out. Their project will develop and test a learning module and explore the role of visualization in learning probability concepts by comparing visual and non-visual versions of that module.
The resulting new methods will have obvious applications for educators and students, the researchers state. “The most direct applications will be for statistics courses, but the general method we are exploring could be adapted for many math concepts,” Starns adds.
One basic motivation for the makeover of statistics coursework, the researchers point out, is that proper reasoning involves calibrating one’s level of confidence that a hypothesis is true to the strength of the evidence available to support the hypothesis. The equation known as Bayes Theorem achieves this calibration, but many students find that equation confusing. The new project will teach students a simple visual method that is analogous to Bayes Theorem and then use this method to help students develop an intuitive understanding for how the equation works.
The researchers plan to test their new visual method against standard equation-based teaching methods in a classroom setting, and will make course materials used for it available for other educators and researchers. More broadly, they say, findings may reveal principles that can guide future efforts to create instructional methods that make difficult mathematical concepts more accessible to students, especially those who struggle with math.