Institutions are increasingly turning to predictive analytics to help determine if students will enroll, and if so, whether or not they’ll need support to stay on track for graduation. But this data use begs the question–are decision-makers using the data ethically?

The pressure to recruit and retain students grows daily in higher education, where institutions strive to ensure students earn diplomas.

Predictive analytics–analyzing past student data to predict various things about current and prospective students–can help institutions meed enrollment and financial goals, according to a new policy paper from New America. Because without ethical practices, the use of student data could end up hurting students’ academic progress instead of helping it.

“For example, without a clear plan in place, an institution could use predictive analytics to justify using fewer resources to recruit low-income students because their chances of enrolling are less sure than for more affluent prospective students,” according to the report, authored by Manuela Ekowo and Iris Palmer.

(Next page: 5 guiding principles of predictive analytics)

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.

About the Author:

Laura Ascione

Laura Ascione is the Editorial Director, Content Services at eSchool Media. She is a graduate of the University of Maryland's prestigious Philip Merrill College of Journalism. Find Laura on Twitter: @eSN_Laura