Page 29 - Campus Technology, May/June 2018

DATA ANALYTICS dian schaffhauser The Rocky Road of Using Data
to Drive Student Success
The California State University system has hit its share of potholes as it tests predictive analytics to forecast student performance in high-failure-rate courses. Here are its lessons learned.
THE PREMISE is fairly simple: If colleges or universities could just identify students most at risk of failing a required course based on a pre- dictive model, faculty and advisers could reach out and lend helping hands. But the road to student success is often pitted with potholes.
And California State University is finding its fair share as it pursues a pilot project that grew out of the university system’s “Graduation Initiative 2025.” This is an ambitious plan to increase graduation rates for all CSU students while eliminating equity gaps for under-represented minorities and Pell-eligible students. For example, the four-year graduation rate for freshmen is pegged to increase from 23 percent in 2017 to 40 percent by 2025; the six-year rate is expected to rise from 59 percent to 70 percent.
Much of the focus for the 2025 program is to eliminate bottlenecks — dumping placement exams, removing non-credit-bearing remedial courses, helping students succeed through their math and English requirements and focusing on waitlisted courses — classes with high demand and low success rates.
The Office of the Chancellor runs an Academic Technology Services
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CAMPUS TECHNOLOGY | May/June 2018
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