Work

Using machine learning to identify the most at-risk students in physics classes

Year: 2020

Type: article

Abstract: 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.

Source: Physical Review Physics Education Research

DOI: 10.1103/physrevphyseducres.16.020130

Authors: Jie Yang, Seth DeVore, Dona Hewagallage, Paul Miller, Qing X. Ryan, John Stewart

Institutions: West Virginia University, California State Polytechnic University

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDGs: Quality education, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,000