by Adam Zewe

Even though progress has been made over the past decades, gender and racial disparities in STEM (science, technology, math, and engineering) fields continue to persist.

A 2021 Pew Research study found that only 9 percent and 8 percent of STEM jobs are held by Black and Hispanic workers, respectively. And while the study found that women hold 50 percent of all STEM jobs (including health-related jobs), the percentages are far lower for jobs in physical sciences (40 percent), computing (25 percent), and engineering (15 percent).

Could machine learning help researchers better understand the factors that contribute to those disparities? Or are machine-learning tools partly to blame for the gender and racial discrepancies in STEM? Haewon Jeong, a postdoctoral fellow in the lab of Flavio Calmon, Assistant Professor of Electrical Engineering at the Harvard John A. Paulson School of Engineering and Applied Sciences, is embarking on a research study to explore both questions.

Jeong received a 2021 Harvard Data Science Initiative Postdoctoral Research Fund award for the project. She will collaborate with Nilanjana Dasgupta, Professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst, who studies implicit bias and stereotypes in STEM education, and Muriel Médard, Cecil H. and Ida Green Professor in the Electrical Engineering and Computer Science Department at MIT.

“We will use machine learning tools to find out what factors feed into implicit bias about science and math in middle school students,” Jeong said. “Because machine learning can pick up subtle patterns that humans often miss, we may be able to use it to detect students who are likely to fall prey to implicit bias and may be at risk for giving up on more advanced math classes in school.”

Jeong and her collaborators will rely on a dataset Dasgupta gathered from a five-year longitudinal field study at 10 U.S. middle schools. Middle school is a critical time to focus on STEM education, Jeong said, since students are developing stronger critical thinking and problem solving skills while also beginning to consider future career paths.

That survey data was collected from 3,000 students in grades 7 to 9, and their parents. It gathered demographic and socioeconomic information and included questions about student self-confidence in math and science classes. The surveys also captured students’ scores from implicit association tests (IATs), which measure subconscious attitudes and beliefs by asking individuals to pair concepts (such as male and female) with attributes (such as logical).

The survey also asked parents for their perceptions of how proficient their children are at STEM subjects, how hard they work in STEM classes, and how much they enjoy them.

By building machine-learning models to analyze that dataset, Jeong hopes to reveal factors that contribute to students’ biases about STEM education and careers.

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