Data Discrimination in a Sea of Diversity: Exploring the Ethical Implications of Student Success Analytics

The rise of student success analytics and machine learning methods has provided colleges and universities insights that have never before been available. Forecasting at-risk students in a single course, or in an overall course of study before it is too late, serves the student and the institution alike. Inferential insights help with retention, resource allocation, student interventions, and ideal course designs, and they provide the opportunity for more personalized learning experiences. However, insights are only as good as the data that inform them. Left unchecked, dirty or misinterpreted data can cause more harm than good. Conversely, clean and straightforward data may still prove to be an inadequate measure of students’ academic well-being. Scholarship on the topic of data ethics has spanned disciplines, but the implications of data discrimination in higher education are relatively uncharted territory. In a sea of data, student success analytics strategies may actually result in unintended consequences benefiting some while harming others. This panel brings together instructional design, research, and practitioner perspectives to present an overview of the literature on data ethics as it relates to higher education analytics. The brief overview will set the stage for a guided discussion and will begin to create community in an effort to promote equity and employ data insights in the beneficent way in which they were imagined.