Query
Template: /var/www/farcry/projects/fandango/www/action/sherlockFunctions.cfm
Execution Time: 3.58 ms
Record Count: 1
Cached: Yes
Cache Type: timespan
Lazy: No
SQL:
SELECT top 1 objectid,'cmCTAPromos' as objecttype
FROM cmCTAPromos
WHERE status = 'approved'
AND ctaType = 'moreinfo'
objectidobjecttype
11BD6E890-EC62-11E9-807B0242AC100103cmCTAPromos

Predictive Analysis of Student Data: A Focus on Engagement and Behavior

Student Success Assessment, Evaluation, and Research Enrollment Management Orientation, Transition and Retention Technology AVP or "Number Two" Mid-Level Senior Level VP for Student Affairs
April 1, 2017 Michelle Burke Amelia Parnell Kevin Kruger Alexis Wesaw

With public scrutiny over the value of higher education increasing, colleges and universities are turning to business intelligence practices to improve outcomes. In higher education, institutional performance is often centered on student enrollment and retention, with an ultimate goal of students’ timely persistence to a college degree. As a result, colleges and universities are considering how to use data to intervene proactively with students who are at risk for poor academic performance or low institutional engagement. Many institutions have adopted data analytics practices to forecast operational needs and enrollment trends, and are now applying the use of predictive analytics directly to student success initiatives.