It’s estimated that universities across university suffer from a 30% dropout rate, costing a significant amount of money.  Being able to reduce this figure therefore is a big issue for the industry.

A team of researchers from the Universidad de Barcelona recently released a study aiming to reduce these levels.  They developed a tool that they believe accurately predict student dropout rates by utilizing machine learning.

“Nowadays, the role of the tutor is more important than ever in order to prevent students from leaving the university and improve their academic performance. The research proposes a system based on objective data to take hidden information which is important for the students’ academic data and therefore, to help teachers to offer their students a personal and proactive orientation,” they say.

Preventing dropouts

Firstly, the researchers wanted to test whether they could predict whether a student will continue into their second year or not, based purely on their results in the first year.  They applied a total of five different algorithms to date from mathematics, computer science and law degree data.  The best of these was able to predict drop out rates with an accuracy of 82%.

The authors wanted to improve upon previous approaches that were based primarily around statistical models that usually used qualitative data gathered from interviews.  Whilst this approach has merit, it often fails to take account for the changing circumstances of the student.

“However, machine learning techniques have a predictive use based on objective data, which makes them more adaptable to new data,” the researchers say.

They believe that their machine learning based approach will offer academics and institutions a ‘warning’ about students that are at risk, maybe even before they enroll on the course.

It might also prove able to predict the grades those students might earn in future courses, thus allowing teachers to take a pre-emptive approach to giving advice and support to them.  It’s very much part of an ongoing project of work.

“The following step is to analyze –from an educational perspective- how to use this tool, how to assess its impact and develop a computer application prototype,” they conclude.