The research lays out a new framework with key questions higher-ed leaders should ask as they determine how to use predictive analytics in an ethical manner. That framework includes five guiding practices.
1. Have a vision and plan. Convene key staff to make important decisions. Consider the purpose of predictive analytics, the unintended consequences of predictive analytics, and the outcomes to measure when developing the plan.
2. Build a supportive infrastructure. Communicate the benefits of using predictive analytics and create a climate where it can be embraced. Develop robust change management processes. Assess institutional capacity.
3. Work to ensure proper data use. Ensure data are complete and of high enough quality to answer targeted questions. Ensure data are accurately interpreted. Guarantee data privacy.
4. Design predictive analytics models and algorithms that avoid bias. Design predictive models and algorithms so that they produce desirable outcomes. Test and be transparent about predictive models. Choose vendors wisely.
5. Meet institutional goals and improve student outcomes by intervening with care. Communicate to staff and students about the change in intervention practices. Embed predictive-driven interventions into other student success efforts. Recognize that predictive-driven interventions can do harm if not used with care.