A data-driven, Readiness for Learning approach to supporting learners return to school following COVID-19
These are, as so many have already commented, unprecedented times.
Understanding the impact of the past three months on our children and young people is crucial in helping schools plan what their return to face-to-face learning needs to look like – from the curriculum to the communication with parents/carers.
Planning for recovery
We feel very fortunate within Clackmannanshire – a small local authority in central Scotland – to have been well on in our implementation of our trauma-informed approach to closing the poverty-related attainment gap – Readiness for Learning (R4L). This meant that all of our educators had a core knowledge about the impact of stress and trauma on brain development, and crucially, learning.
We have been able to support our schools to develop this understanding into clear trauma recovery plans that take account of the needs of their learners as they return to school and which operationalise a number of key features – namely the need for consistent, ongoing regulating activities provided by and in the presence of a safe, regulated adult. This approach is informed by Bruce Perry’s Neurosequential Model in Education (NME), on which the R4L approach is based.
A data-informed approach
To support this planning, we have undertaken a large-scale digital survey of the mental health and wellbeing needs of our learners through the use of the Viewpoint computer assisted self interviewing (CASI) platform. Using this tool has allowed us to quickly and easily survey a large proportion of our learners and their families to understand how they are functioning currently, and whether this is better or worse than how they normal cope with everyday life. We’ve combined this with the Strengths and Difficulties Questionnaire (primary and secondary age), and the Brief Early Skills and Support Index (BESSI) (pre-school). The survey is set up to allow us to track learners’ progress over time and to add in data from teaching staff once they return at the start of the new session.
Currently, we have data relating to 518 primary learners (from 14 primary schools) and 110 pre-schoolers (from 6 nursery classes). What this is showing is that we have a significant proportion (roughly 20-25%) who are displaying difficulties in a number of key areas that will impact on their ability to function effectively on their return to the classroom – i.e. attention, behaviour regulation (e.g. sitting still when asked) and anxiety. Further, the number of pupils identified as falling into the High/Very High category for Total Difficulties is far higher than the population norm (58% compared with population average of 7.8% for primary pupils, 51% for pre-school pupils where the population average is 4%).
A localised response
This data was crucial in accurately understanding the emotional needs of our pupils and providing schools with the information they require to plan targeted interventions to support individuals and groups of learners on their return. Planning that has, so far, paid off, with 69% of our schools reporting that pupils are more settled than they were expecting, and 19% reporting a mixed picture (i.e. some more settled, some less so).
Only 12% have found pupils to be less settled than they expected. Being able to track learners over time using the Viewpoint system will allow us to demonstrate the impact of these interventions and ensure that we remain on track towards our goal of closing the poverty-related attainment gap by supporting the mental health and resilience of our learners first and foremost.
While this data is highly context specific and drawn from an area of high socio-economic deprivation (meaning we believe our population norm would be slightly higher than the norm anyway), it highlights the need for good quality, localised data to be gathered across the country to better understand the impact of COVID-19 on our learners in order for supports and interventions to be designed and targeted to where they are needed the most.
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