We improve retention rates. We minimise student withdrawals. We maximise student success.
Every year students drop out of college courses.
In Scotland 22% of students drop out of college courses every year.
Many of these withdrawals are preventable.
Students at risk of drop-out who are:
identified early; and
engaged and supported to remain on the course,
...can achieve a successful outcome for the individual and the college.
But how does your organisation do this?
This is where Beagle Evolver can help. Our bespoke solution, Evolver, uses multiple data points and the latest predictive analytics and machine learning technology to predict the probability of student withdrawals.
How it works
Evolver uses data that colleges already collect to order students by probability of withdrawal - with the purpose of engaging those most at risk.
The data we collect is generated from student activity, for example:
Their Virtual Learning Environment usage (logons, activity, forum engagement)
Their Library usage
This data is extracted to a database which we will then run machine learning over to create the probabilities of success, partial success and withdrawal.
We Grow Trees!
We use machine learning tools to create multiple decision trees which decide whether a student is likely to withdraw.
On their own, decision trees can be flawed. But multiple trees, that learn from the mistakes of the last, create a powerful tool that provides staff with accurate, rich data.
We will get our Beagle Bots to automate your contact strategy.
Anything that is time consuming and repetitive, our Beagle Bots will fetch it for you.
Evolver assesses the data to determine the probability of withdrawal risk and students are allocated a rating of between 0 and 1000, with 0 representing the highest risk of withdrawal.
Staff can then decide whether intervention is required to prevent withdrawal.
The predictors used by Evolver are customisable by the staff - allowing them to decide on the key factors.
Evolver facilitates intervention. Staff can use the information extracted to feedback to students and let them know areas for improvement.