Marist College, in Poughkeepsie, NY, is using predictive analytics to determine whether students are likely to drop out of their courses.
The college has developed an early warning system for students attending online and hybrid (traditional lecturing with homework completed online) courses. This system identifies students at risk based on factors such as:
- reading patterns (clickstream analyses)
- time spent reading
- forum participation
- length of questions/comments
- time to complete exercises
The system uses the data available to it to keep learning about leading indicators that best determine a student at risk.
When a student is flagged by the system, an alert is sent to the relevant professor who can intervene before it’s too late.
