Harnessing blended learning data to address the education and attainment gap as a result of Covid-19

One of the most significant impacts of COVID-19 on the long-term future of the UK has been on the education of our young people.

Lockdown is understood to have created two significant types of impacts on students. The first is the attainment/progress gap in the sense of lost learning. Contributing factors to the level of impact are likely to include the particular school's quality and quantity of remote education (or face-to-face provision for vulnerable/key workers' children), the quality and quantity of home learning by parents, and a variety of home background factors including socio-economic, physical environment, access to technology and resources etc.

The second type of impact is psychological/interpersonal/developmental. Young people, particularly those who have not been in school throughout lockdown may have also lost acclimation to the school learning environment, structures and routines; interactions with peers; and interactions with teachers. This lost acclimatisation, alongside other difficulties of lockdown life, may have affected their ability to recover lost learning, wider psychosocial development, and mental health/wellbeing.

These gaps and factors are currently hypothesised but the extent to which they have affected young people over lockdown is not yet well understood or evidenced. Gathering this understanding and evidence is crucial to ensure the current cohort of students do not become a lost generation, with long term detrimental effects on them, wider society, and the national workforce and economy.

We have a unique opportunity at this moment in history to analyse learning and development gaps in school children through the use of remote/blended learning data. Never has so much educational activity taken place on digital learning platforms as over the past few months.

Sparkjar -- our world-leading UK-based blended learning platform for schools -- saw +700% in usage during lockdown. Throughout lockdown, our two testbed secondary schools using have run full teaching timetables throughout, exchanged 170,000+ messages, set 9,000+ assignments, and 69,000+ pieces of work have been submitted by students.

Not only is this quantity of data remarkable, but its quality and completeness are unprecedented because the schools have been using Sparkjar as their primary means of interaction for a number of months. Never before has there been a data set as rich to analyse patterns in learning and behaviour in the context of mainstream education.

At the same time, we are at a point where the fields of machine learning and big data permit an unprecedented depth of analysis of large data sets.

Our vision is to harness the power and value of this unique data, providing schools with a realtime dashboard of insights into student learning, progress, behaviour and mental wellbeing. We call this Sparkjar Realtime Insights (SRI). We will leverage cutting-edge machine learning, expert systems, statistical analysis and data visualisation in order to spot patterns and anomalies, and provide realtime actionable insights at three levels of granularity: individual student, student groups, and whole-school. This will represent a quantum leap in school data analysis for schools when compared to current best practice of termly manual reporting and spreadsheet analysis.