Date of Award
5-2017
Document Type
Dissertation
Degree Name
Doctor of Education (EdD)
Department
Executive Leadership
First Supervisor
Guillermo Montes
Second Supervisor
James Hurny
Abstract
During an era of accountability for institutions of higher education, it is increasingly important that leadership prioritize student success outcomes. Graduation and retention rates of new students have remained stagnant for years despite investment in the billions of dollars each year to affect outcomes. Predictive analytics are tools organizations can use to identify at-risk students and target them with success interventions prior to them showing signs of academic difficulty. This study modifies the Demming Plan-Do-Study-Act model by adding predictive analytics at the planning stage to make the model proactive. Institutional research and effectiveness professionals at colleges and universities across the United States were surveyed to determine the extent predictive analytics are being used. Sixty-one percent of colleges are using predictive analytics, and 88% of these institutions are using predictive analytics to identify at-risk students. By connecting at-risk student models to the strategic planning process, college and university leaders have the ability to revolutionize the academic experience by suggesting degree programs, courses, and success programs based on a student’s likelihood of success.
Recommended Citation
McLean, Mary, "Continuous Improvement in Higher Education: A Change Model Using Predictive Analytics to Achieve Organizational Goals" (2017). Education Doctoral. Paper 304.
https://fisherpub.sjf.edu/education_etd/304
Please note that the Recommended Citation provides general citation information and may not be appropriate for your discipline. To receive help in creating a citation based on your discipline, please visit http://libguides.sjfc.edu/citations.