syddansk erhvervsskole (SDE) uses data
to prevent drop-out

 

With the goal of increasing the completion rate in its students, SDE College has created a data-driven culture using the large quantities of data the college collects every day to say something about how many students are likely to drop out, and for whom the risk is the greatest. They additionally gain insight into what they have to do in order to prevent drop-out, e.g. what measures to target at individuals, what action is to be undertaken overall and when.

As part of this work, SDE College uses machine learning and so-called churn analyses, which indicate which students are in imminent danger of dropping out, why they are in the danger zone and what SDE College can do to prevent drop-out.

challenges for
vocational colleges

With an increased focus on drop-out rates and the government seeking to get more students to do vocational courses, SDE College has actively entered the fray in order to increase their completion rate. It is a struggle, however, and it is hard to do very much about it if you don’t have insight into the students’ behavior. But if you do, there’s a lot you can do to lower the drop-out rate.

the data-driven
vocational college

 

For whom is the risk the highest?
SDE College has 5,100 students, and data has been collected for each of them, as well as for many other students from previous year groups. They used not to gain any insight regarding student drop-outs until they actually left, but by comparing their large quantities of data they can predict which new students are in imminent danger of dropping out, and can proactively deal with the situation.

 

New initiatives so courses are completed
Thanks to machine learning and churn analysis, SDE College can now identify and be proactive about students at high risk of dropping out from their course. They can be aware of the students in question in the form of absence and the person’s actions, but they can also introduce new measures to help students complete their course.


“We want to create the best parameters for our students to stay with us and become the best in Denmark. Linking up people and technology gives us the best of both worlds. With our churn analysis our contact teachers get a fact-based tool for creation of a dialogue with each student. We can at the same time make sure we are pursuing the specific activities that make a difference to students in their everyday life.”

Anja Domaiski, Managerial Assistant, SDE College

machine learning
at sde college
from a technical standpoint

The solution for SDE College involves an add-on module for their existing data warehouse in the form of a machine-learning engine that executes and carries out a number of predictive analyses and returns the results to their data warehouse. The results include a number of descriptive variables with different weightings plus a combined model that can be used to predict the drop-out rate for the students in question. The results are also displayed on a dashboard, on which data is presented visually and comprehensibly, so contact teachers who are not otherwise used to data can understand it and act on it. With the aid of this solution SDE College is able to partially identify which students are at a high risk of drop-out, and at the same time explain why they in particular are likely to drop out.

  • Data integration
  • Data warehouse
  • Machine learning
  • Qlik Sense Dashboard

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