Why pupils hate real-time data

Anyone parenting children might relate to the notion that kids, especially teens, think adults know nothing.

‘You don’t understand’ seems to be one of their favourite throwaway comments.

The teenage brain is complex, and the teen years can be a tumultuous time; there are normal behaviours and problems that all children struggle with, but some display deeper issues.

If being a teenager is hard, teaching them is even harder.

Teachers do their best to look out for obvious changes and support pupils through their education.

It can be easy to miss things, however.

Children are rarely forthcoming in explaining what is troubling them, and without being telepathic, teachers often have very little to go on.

In addition to factors outside of school that a child could be struggling with, children frequently struggle with school itself or aspects of it. Uncovering these challenges and putting something in place to alleviate them can really help the school experience be a more positive and productive one.

Data holds the key to better understanding

Schools hold a wealth of information about their pupils.

When correlated together, this data has the potential to paint a very accurate picture of problems as they emerge. Combining assessments, attendance and wellbeing data, for example, presents a much more holistic view of the student’s world.

Let’s look at an example: if a child is usually a good student but starts showing disinterest in school or has falling grades, it could be a sign of deeper problems. Whilst disinterest in school alone is not uncommon or necessarily reason for concern, there could be something at the heart of falling grades.

How to use data to uncover the root cause

Has the child with falling grades been more absent recently? If so, there could be a pattern to that absence e.g. multiple absences on a certain day of the week?

The Government has reported, for example, that schools are seeing a huge increase in pupil absences on Fridays and around half of those that are persistently absent are due to reasons other than illness.

If there is no additional absence issue, it could be something else at the heart of falling grades. By comparing timetable data to attendance data, for example, it could be that there is a particular subject area or peer group that the child is trying to avoid, and this is now affecting their performance in class.

Has there been any out-of-character behaviour, such as being disruptive in class or withdrawn from things they normally love? Is this behavioural change more prevalent in certain lessons?

By correlating timetable data with behaviour data, it could be that this uncovers a potential clash with a particular teacher (by adding staff data, too) or student (by checking seating plans).

Has a student had visits to the school nurse/sick bay/matron?

Was there any pattern to these visits e.g. about the same thing or during a particular lesson?

By correlating timetable and medical data, it could again be a ruse to avoid a particular situation, lesson, teacher or pupil.

Turning intuition into actionable insights

Parents and teachers know in their gut when a child isn’t themselves, but turning that gut feeling into something actionable is the challenge.

This is where the gut feels turn into a data-driven steer.

Independently of each other, student data points may not be reason for concern. It is only when they are correlated and manipulated that the full story emerges, giving a reason to flag the issue to others.

If the picture isn’t yet crystal clear from the data, you may decide not to act, but instead put measures in place with colleagues for further data recording on specific things until you are surer, so an appropriate and more considered course of action can be taken.

The picture is only going to get more accurate

In the future, Machine Learning (ML) could combine student data silos in order to build an automated score of how concerned teachers should be about students or groups.

Think of it as a tagging system whereby if children meet a criteria, the system will recognise the pattern, flag it and advise tighter monitoring, for example. 

The systems could even go as far as proposing interventions based on what the data is showing. This is enabled by either getting teachers to record what interventions they have done against certain scenarios or by getting evidence-based input into the system from an educational expert.

Making a difference

Modern technology has the potential to really change the dial in an educational setting.

If used more widely, we can really make a difference in children’s lives.

Schools can spot trends or challenges before they become really big issues and engage with those children and their families to find a solution.

On the surface, children might not feel they want to be understood.

I beg to differ.