Measuring the Bias in Predictive Models of Student Success
In education, learning analytics, fueled by student data, is used to develop early warning systems to help learning institutions identify when a student might need extra support to succeed in school.
There is a lot of promise in being able to predict success and identify when struggling students might need help, but there are strong intersections with privacy questions that relate to using student data to accomplish that. Recent debate has centered on whether students should be given the opportunity to opt out of having their data shared.
This research will look at the population characteristics of students who might opt out of allowing their data to be used for predictive modeling, whether allowing opt-outs will have an effect on the accuracy of predictive models, and whether the process will lead to algorithmic bias.
The amount of the award is $49,848 for UMSI for the project period. The grant is funded by the Spencer Foundation.