Predictive analytics is a rapidly growing field that has the potential to shake up how schools and universities approach student retention. By leveraging their data, schools and universities can identify students who are at risk of dropping out and provide them with the support they need to succeed. In this blog, we’ll explore how predictive analytics can improve student retention rates and some tricks to help you do so.

What is Predictive Analytics?

Predictive analytics is a process that involves analysing data to identify patterns and make predictions about future outcomes. In the context of student retention, predictive analytics involves analysing student data to identify students who are at risk of dropping out. This can include data such as grades, attendance records, and student demographics.

By identifying students who are at risk of dropping out, institutions can provide them with tailored support to help them succeed. This can include interventions such as tutoring, academic advising, and mentorship programs. By providing students with the support they need to succeed, institutions can improve student retention rates and help more students achieve their academic goals.

How do you do all of this?

The answer may be simpler than you think. Analytical tools like Domo can help institutions monitor, manage, transform, visualise, and even blend data from multiple sources to unlock hidden insights into what is otherwise untouched data.

Here are a few simple ways an analytics tool like Domo can help schools and universities support their student retention rates:

  1. Identify at-risk students
    Predictive analytics can help educational institutions identify students who are at risk of dropping out by analysing student data such as grades, attendance, and engagement. This allows institutions to intervene early and provide support to help these students stay on track.
  2. Analyse student feedback
    Institutions can analyse student feedback from surveys, focus groups, and other sources so they can understand student needs and preferences. This information can help institutions improve programs and services to better meet student needs and improve retention.
  3. Track institutional goals
    Key performance indicators (KPIs) related to student retention, such as graduation rates and student satisfaction should be measured and evaluated over time. By monitoring these KPIs in real time, institutions can quickly identify areas for improvement and take action to improve retention.
  4. Personalise support
    Finally, institutions should personalise support for students by analysing data such as student demographics, academic performance, and engagement. As a result, institutions can tailor interventions to meet individual student needs, leading to improved retention.

Conclusion

In summary, predictive analytics is a powerful tool that can help educational institutions improve student retention rates. By using their data, institutions can identify students who are at risk of dropping out, monitor their progress over time, analyse student feedback, track internal institutional goals, and personalise student support. By embracing predictive analytics, institutions can improve student retention rates and help more students achieve their academic goals.