Using machine learning to identify the most at-risk students in physics classes
Jie Yang, Seth DeVore, Dona Hewagallage, Paul Miller, Qing X. Ryan, John Stewart · Physical Review Physics Education Research · 2020
Demographic variables such as gender, underrepresented minority status, first-generation college student status, and low socioeconomic status are not important predictor variables in models to identify students likely to receive a D, F, or withdraw in their introductory physics course.