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 bysending 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 also completed the survey. We identified that institutional trust, concerns with the amount of data collection versus perceived benefits, and comfort with instructors’ data use for learning engagement 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 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.
Learning analytics defines itself with a focus on learner data, with corresponding goals of understanding and optimizing student learning. In this regard, learning analytics research, ideally, should be characterized by studies that make use of data from learners engaged in education systems, should measure student learning, and should make efforts to intervene and improve these learning environments. However, a common concern among members of the learning analytics research community is that these standards are not being met. In the current study, we review a large and comprehensive sample of research articles from the proceedings of two recent Learning Analytics and Knowledge conferences, the premier conference venue for learning analytics research. We find that 36.3% of articles do not analyze data from learners in a formal education system, 70.5% do not include any measure of learning, and 91.4% of articles do not attempt to intervene in the learning environment. We contrast these findings with the stated definition of learning analytics, and infer, like others, that scholarship in learning analytics research presently lacks clear direction toward its stated goals.
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