Education is without a doubt one of the most crucial parts of a young person’s life – and all children and young people should be confident that they are receiving the best education possible, regardless of their location or family’s socio-economic standing.
Sadly, however, this isn’t the case. In Scotland, for example, only 4 out of 10 students living in socio-economically deprived areas achieve one or more Higher qualification (equivalent to an AS Level in England). This is compared to 8 out of 10 students in non-deprived areas.
This trend also continues into higher education, where minority students are more likely to drop out of their courses, and less likely to achieve a First or a 2:1, or go onto a graduate-level job after graduation.
Whilst much of the solution to this problem lies in dismantling oppressive structures and stereotypes that put poor or BAME students ‘at risk’, technology can also play a part in making education more equal.
Improving Attainment with Predictive Analytics
Imagine being able to support students more likely to fail, before their struggles have become too serious. Or being able to highlight performance changes in a class, department, or college before issues come to light with end-of-year-exams.
With predictive analytics, all of that is possible.
With predictive analytics, educators can, for example, identify students most likely to fail, or drop out, and (most importantly) intervene before it’s too late. By identifying ‘at risk’ students, educational institutions – at whatever level – can direct support resources to those most in need, when they need them.
This is exactly what is happening across the globe. Georgia State University in the US, for example, has used analytics to identify students with a higher risk of dropping out, with the goal of getting them the help they need to get back on track. Similarly, Arizona State University has seen its graduation rate rise by 20 percent in the 10 years it has been using predictive analytics.
Choosing the Right University with Predictive Analytics
Predictive analytics is not only helping educators improve their retainment and attainment rates, however; it is also helping students choose the right university and courses for them.
A Singapore-based college prep course, Cialfo, for example, has combined traditional tutoring with data analytics to help college applicants choose the right university or college for them. The algorithms look at thousands of different data-points to create a shortlist of colleges most suited to an individual applicant, based on a variety of different factors.
On the flip side of this, we are also seeing universities use predictive analytics when accepting or rejecting applicants. By looking at past data, admissions offices are making decisions based on whether or not a student is ‘likely’ to succeed.
The problem with this, however, is that universities who have failed to adequately support minority students in the past will be less likely to accept minority students in the future – which means they will also be less able to support the minority students they do accept. It creates a never-ending circle whereby minority students can never win.
One way to overcome this, however, is to consider more than just racial, ethnic, or economic background when using predictive analytics to analyse students. Educators should also consider other factors which influence success, such as the amount of financial aid – either from the government, university, or student’s family – a student is likely to receive. This is because students who are under financial pressure, or have to work multiple jobs to support themselves through university, are more likely to be forced to drop out. This is especially true in America where the cost of tuition is astronomically high, and student loans are hard to come by.
Overall, whilst predictive analytics will never completely close the attainment gap, it can help educators make decisions that will better support the students most in need.
If you would like to know more about predictive analytics and how it can help you and your students, please contact us today on email@example.com, or sign up for one of our Analytics Everywhere workshops.