In a paper published on the preprint server Arxiv.org, researchers affiliated with West Virginia University and California State Polytechnic University investigate the use of machine learning algorithms to identify at-risk students in introductory physics classes. They claim it could be a powerful tool for educators and struggling college students alike, but critics argue technologies like it could harm those students with biased or misleading predictions.
Physics and other core science courses form hurdles for science, technology, engineering, and mathematics (STEM) majors early in their college careers. (Studies show roughly 40% of students planning engineering and science majors end up switching to other subjects or failing to get a degree.) While physics pedagogies have developed a range of research-based practices to help students overcome challenges, some strategies have substantial per-class implementation costs. Moreover, not all are appropriate for every student.
It’s the researchers’ assertion that this calls for an algorithmic method
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