4 steps to realize the promise of predictive and prescriptive analytics in higher ed
The value propositions behind big data analytics, artificial intelligence (AI), and machine learning (ML) are virtually limitless. Yet education has the lowest adoption of predictive and prescriptive methodology.
How can institutions close this gap? Thoughtful analysis of technology ecosystems and a use case-driven approach to implementation can bring interactive value to students, educators, and academic administrators.
This e-book examines the vast opportunities and benefits to students and educational institutions of implementing:
- Targeted student advising
- Adaptive learning
- Enrollment management
- A flexible education data model and strategy to overcome perceived limitations
While it’s impossible to redesign students to fit into a data model, we can redesign one that speaks to student engagement. Having a flexible, extensible data model and heuristic algorithms with automated updates would go a long way toward creating a dynamic analytics environment through which hidden insights can be gleaned, and retention can be established.