Disparities in Students’ Propensity to Consent to Learning Analytics

Abstract

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. We investigated university students’ propensity to consent to learning analytics through an email prompt, and collected respondents’ perceived benefits and privacy concerns regarding learning analytics in a subsequent online survey. In particular, we studied whether and why students’ consent propensity differs among student subpopulations by sending our email prompt to a sample of 4,000 students at our institution stratified by ethnicity and gender. 272 students interacted with the email, of which 119 completed the survey. We identified that institutional trust, concerns with the amount of data collection versus perceived benefits, and comfort with instructors’ data access were key determinants in students’ decision to participate in learning analytics. We find 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 . Students’ comments corroborate these findings and we discuss the implications of these concerns on educational data collection.