Our investigation found that after a decade of use and millions of predictions, DEWS may be negatively influencing how educators perceive students of color.
Early warning systems that use machine learning to predict student outcomes are common in K-12 and higher education. Wisconsin public schools use an algorithmic model called the Dropout Early Warning System to predict how likely middle school students are to graduate from high school on time. Twice a year, schools receive a list of their enrolled students with
told us they received little training on how those predictions are generated. Students told us the high risk labels are stigmatizing and discouraging. A forthcoming from researchers based out of the University of California, Berkeley, who shared data and prepublication findings with The Markup found that
“They just handed us the data and said, ‘Figure it out,’ ” said Sara Croney, the superintendent of Maple School District. In the city of Racine, middle schools to select which students would be placed in a special “Violence Free Zone” program, which included sending disruptive students to a separate classroom. once used
existed and were concerned that an algorithm was using their race to label them and predict their future “It makes the students of color feel like they’re separated … like they automatically have less,” said Christopher Lyons, a Black student who graduated from Bradford High School, in Kenosha, in 2022. Kennise Perry, 21, attended Milwaukee Public Schools, which are 49 percent Black, before moving to the suburb of Waukesha, where the schools are only 6 percent Black.
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