2. Assume you are a lifeguard on a crowded beach. With no
“data” or “predictive model”, you usually wait for someone to
scream “HELP” before acting. With analytics, if you could see
that someone’s heart rate was increasing, you may
proactively swim out to help them.
From a causation standpoint, you don’t know if they are
having a heart attack, caught in the undertow, or if they saw
a shark. You just know there are signs of trouble. Once you
get there, use your training to determine the cause and try
and solve the problem.
That’s one way to think about using analytics with students
who are at-risk.
- Mike
3.
4.
5.
6. Predicts student outcomes
thereby enabling
interventions that promote
persistence and completion.
Links individual student
needs to tailored advising
services and personalized
interventions.
Examines student behaviors
and needs and ties these to
patterns of engagement
with services, in order to
develop “successful” and
“at risk” profiles.
Informs teaching and
learning strategies to
improve student outcomes.
Enables early interventions
and timely feedback so that
interventions are proactive
and not reactive.
…for example – I want predictive analytics that:
One of the top three opportunities for predictive analytics
was the identification of good teaching and learning
strategies and early intervention models.
The most valuable data predicts how best to serve
students and helps students help themselves…
