To ease implicit bias, oceanographer Drew Talley, PhD, tackles the mindset behind it—and helps underrepresented students succeed.
Associate Professor of Environmental and Ocean Sciences,University of San Diego
PhD in Biological Oceanography, BS in Biology
Before shifting to the sciences, University of San Diego professor Drew Talley, PhD, studied philosophy for a while. That deep thinking has helped inform his approach to teaching courses in environmental and ocean sciences. “I always have my students focus on thinking about what they’re doing and why,” he says.
Among the most damaging beliefs that Talley wants students to consider is implicit bias, specifically as it relates to women and minority students being unconsciously perceived (by themselves or others) as less capable in certain subjects. “A lot of it is a culture problem,” says Talley. “Girls have been taught that math is scary and perhaps too challenging. And I have found that many students from underrepresented groups seem to have more fear around math and science, as well.”
He has seen this both in college and in poorly-resourced schools, which is why he has spent more than a decade as Science Director for the nonprofit Ocean Discovery Institute, which educates economically disadvantaged children about science.
In his Environmental Data Analysis course at USD, the perils of implicit bias are often compounded by the course’s central focus on statistics. Here, he shares the three practices he uses with students in this course to face these issues head-on—and turn students’ fear into confidence.
“Statistics can feel difficult and irrelevant, and I know it. Making statistics more broadly relatable helps students get a leg up in an area that may be especially challenging to them. Being in a field that is very white and male, I hope that broadening access will lead to greater diversity.”
-Drew Talley, PhD
Course description: This course will provide an introduction to the fundamentals of experimental design and quantitative analysis of data in environmental sciences. Students will learn to form and test hypotheses through the lens of Environmental and Ocean Sciences using a number of basic statistical tests, including t-tests, ANOVA, linear regression, correlation, and non-parametric statistics. Specialized statistics may be covered in later class meetings. Students will learn the basics of using R to analyze data.
Talley’s 3-point plan for making statistics “add up” for everyone
The Environmental Data Analysis course Talley teaches is loaded with statistics, but he does not teach the way he was taught (with mounds of data on seemingly irrelevant concepts). Instead, Talley uses a more philosophical approach, first taking steps to reduce implicit bias, then providing examples that make material relatable, and finally providing practical approaches that make the subject easier to grasp. (Talley has built this course with Dr. Jennifer Prairie, a fellow USD professor who shares his passion for making STEM accessible to everyone.)
“Statistics can be daunting,” says Talley. “I never understate the difficulty of the material. But I want students to know that while this topic may be tough, I have full confidence that they can master the material.”
Here, his three approaches to making that happen:
1. Raise awareness of implicit bias (with studies and statistics)
Talley broaches the subject of implicit bias on the first day of class. “We are all vulnerable to our own biases,” he tells students. The best thing they can do, he explains, is to make implicit bias explicit. Here are three ways he does that:
- Call awareness to the seemingly subconscious assumptions that students are making. Let them know that it is nothing to be ashamed of, even as they take steps to change it.
- Use examples throughout the course to keep the idea of bias top of mind. “When we talk about how to interpret graphs, I will often use data from studies showing, for example, how underrepresented African-Americans are in the field of oceanography or how women are paid less money and rated as less competent than men in similar jobs, and how student evaluations are biased against women and people of color.”
- Present data from studies conducted by researchers from underrepresented populations. “Show, don’t tell,” he says. “For example, I recently used data from the work of a Latina scientist [on estuaries in Baja California] and made a point of including her photo so students could consciously—or even subconsciously—connect the image with the work.”
2. Use friendly, hands-on materials (like candy) for statistics experiments
Talley kicks off one of the first activities of the semester by telling students that they will be gathering their own data by using M&M candies in regular, peanut, and peanut butter flavors. This begins when he passes out a single-serving-size bag of each flavor to each student and tells them they will conduct their own hypothesis tests, with chi-square tests, ANOVA (analysis of variance) tests, and t-tests using the M&Ms. (Most teachers would use prepackaged data containing computer-generated numbers on a spreadsheet.)
“We spend time ‘censusing’ those bags,” Talley says, “dividing [the M&Ms] into different color groupings. M&Ms form a friendly, relatable example that we return to again and again throughout the semester. This allows me to make explicit connections to data from real experiments.” For example, he might introduce data on beetles, saying, “This is just like with M&Ms, only in this case the species of beetle is the same as color.”
“The fact that it’s data they collected matters, too,” he adds. “They are not just being handed the data.” In this way, they are using the scientific method and tools, which proves to them that they have the ability to be scientists.
Perhaps the best perk? Students get to eat their M&Ms at the end of the day.
3. Help students make “recipe cards” and flowcharts for testing code
Talley’s goal is to have students understand and be able to use statistics—not memorize and regurgitate information.
In Talley’s data class, students use R, a software environment for statistical computing and graphics: It is open source, increasingly popular, and free. Talley says that R allows students to analyze data and make graphs quickly, once they understand how it works. Unfortunately, he has found that students almost universally struggle with figuring out which test to use and what line of code is required to run the test they selected.
Talley does not make students memorize these things because, in the real world, they will use their tools and experience to choose the right test, run it, and interpret the results. They will not be locked in a room with no reference materials, as they might be during an exam. So rather than having them memorize the code, he helps them create flowcharts and “recipe cards” that they can reference during tests (and after completing the course) that will help them get to the analysis stage more quickly. (See sidebar for examples.)
Student reactions to Talley’s approach
Students appreciate Talley’s methods. “I was really dreading taking this class, but it is required for the major,” one student recently wrote in a course evaluation. “I ended up really enjoying it! The hands-on work in the greenhouse—and the candy 🙂 —helped me wrap my head around this stuff.” Another student praised the utility of the tools that Talley helps students develop: “I ended up using the R recipe cards for doing a project in one of my other classes.” That may not be statistically significant, exactly, but it sure counts as a win.