Before we measure anything, including wellbeing, we need to define what it is we are measuring. In many systems and jurisdictions this is defined for them through frameworks and policy often resulting in highly jargonistic or ambiguous phrasing and measured through blunt instruments such as attendance, frequency of behaviour incidents and annual student surveys. While there is utility in some of these tools, often for system reporting and funding accountability more than the school, it often results in additional pressure placed on schools and artificial targets or goals set for schools.
These artificial wellbeing targets in turn influence the programs that schools put in place, resulting in wellbeing programs that hit the target but miss the point while the programs that are really needed are overlooked because they won’t turn the needle on the metrics employed. For example, many schools put in place programs that are designed to build resilience, reduce anxiety or build self-worth. They put these programs in place because they know their students well and can see the need. These programs will make little to no impact on an attendance target so, when schools are asked to make an evidence informed decision about whether to continue these programs or not, they are often painted into a corner where they need to abandon a program that student need because the data collected is measuring the wrong thing.
To be clear, I am not suggesting that using data to inform wellbeing programs is the wrong approach. Quite the contrary, I strongly advocate for setting the right target and using the best data to inform decision making. So, what then is the right target and best data for measuring student wellbeing?
This is where wellbeing gets slippery and a solid definition is required. A good question to ask at this point is; What do we want our wellbeing program to achieve?
If as part of that answer you include sentiments that put student outcomes first and include things like:
· I want our students to know we care about them
· I want our students to feel safe
· I want our students to have strategies to manage their emotions
Then part of your data set for measuring wellbeing will need to come from student voice.
If you are collecting student voice, here are a few key principles:
· Collect data with a high frequency. A good rule of thumb is that collecting a small amount of information often, is better than collecting a large amount of information infrequently.
· Build trust with the students. Ensure students know why data is collected and what it is used for. And actually use it for what you say you are going to use it for. It can be very detrimental to ask for student voice, the students to provide it and then feel like they haven’t been heard.
· Bring all teachers on the journey by asking for their input and perception. Ultimately, the student voice needs to be deemed relevant and reliable by the teachers who are going to act based on the data, so it is critical that they buy in to how the data is collected and analysed.
· Create the time and space to analyse the data. Ensure whole school processes are in place to act on the data collected in a timely manner. Wellbeing data shifts quickly and does not follow a linear trajectory so if it takes to long to react to the information the students have provided, the moment can be lost.
Measuring student wellbeing can be complex but by putting in place whole school processes and keeping students at the heart of the decisions you make you can have a huge impact on students and teachers.
[ Rydr Tracy is the Head of Education at Life Skills Group and former Director Strategic Priorities at CESE. He is a specialist in evidence-informed practice in educational innovation, with a career focus on strategic change that improves student outcomes. He draws on a rare blend of successful experience in schools, system leadership roles and industry practice – experience that has given him deep understanding of the complexities of the education sector from the classroom to the boardroom and a demonstrated capacity to generate practical recommendations that are grounded in context and evidence. ]