Disparities in Students’ Propensity to Consent to Learning Analytics. Use of university students’ educational data for learning analytics has spurred a debate about whether and how to provide students with agency regarding data collection and use. A concern is that students opting out of learning analytics may skew predictive models, in particular if certain student populations disproportionately opt out and biases are unintentionally introduced into predictive models. This study finds that students identifying ethnically as Black were significantly less likely to respond and self-reported lower levels of institutional trust. Female students reported concerns with data collection but were also more comfortable with use of their data by instructors for learning engagement purposes. Students’ comments corroborate these findings and suggest that agency alone is insufficient; institutional leaders and instructors also play a large role in alleviating the issue of bias.
Warren Li, Kaiwen Sun, Florian Schaub & Christopher Brooks; International Journal of Artificial Intelligence in Education (2021)