Predictive analytics involves using AI to analyze student data and identify patterns that can predict outcomes – such as which students are at risk of failing or dropping out. In education, predictive models crunch data like grades, attendance, demographic factors, and even LMS interaction logs to flag students who may need extra support. This allows schools and universities to intervene early, improving student success and retention.

Real-World Example: Georgia State University (GSU) pioneered the use of predictive analytics in higher education and saw stunning results. GSU developed an early-warning system that tracks hundreds of data points (e.g. missed assignments, course difficulty, financial holds) for every student each day. When the system detects a risk pattern – say a significant drop in GPA or a failing grade in a major-required course – it prompts an advisor to reach out within 48 hours. In the first few years of using this approach, GSU’s *four-year graduation rate jumped by 7 percentage points​. Students were also graduating faster; the average time to degree dropped by half a semester, saving the graduating class millions in tuition costs. Perhaps most impressively, GSU estimates that thousands of additional students graduate each year now who might have otherwise dropped out, thanks in part to timely interventions. This also impacts the university’s bottom line – *every 1% increase in student retention equated to an additional $3+ million in tuition revenue for GSU​. Other institutions have reported similar wins: one community college saw a 6% increase in student retention after 18 months of using predictive analytics tools and targeted support programs, and many colleges are now adopting such systems to boost outcome​.

Benefits for Institutions & Educators:

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