Nine in 10 colleges use some form of statistical analysis to determine retention and learning strategies.
Phil Ice knows numbers never lie.
Ice, the director of course design, research, and development for American Public University System (APUS), has watched retention rates at the 70,000-student online school steadily climb with the continued analysis of in-depth information that shows when a student might be on the verge of dropping out.
If a student’s test scores are dropping, participation numbers are low, and disengagement is evident through various statistics, the numbers suggest that student might not last much longer at APUS.
How can professors and university officials know precisely which students are in danger of giving up on their education? One surefire strategy is to examine how many days have passed since the student last logged onto his or her course website.
If it’s been a while since the online student checked the site for syllabus updates or discussion sessions, APUS’s analytics system will flag the student as a potential dropout.
“We hone in on things such as a student’s perception of being able to build effective community,” Ice said.
APUS professors and instructors use information detailing a student’s online engagement with a comprehensive survey to create a model of student retention and satisfaction, according to the university.
“We’ve been using analytics before it was a buzz word in higher education,” Ice said.
APUS uses IBM’s SPSS Modeler, which uses key student performance, participation, and attendance information in part to measure a student’s social presence, along with a student’s perception of online learning’s effectiveness.
These two factors have proven to be reliable variables telling professors and instructors how likely a student is to drop out of classes.