Up to 17-18% of Estonian youth do not reach secondary education and are dropping out of studies (Eurostat, EHIS). High rate of people with low educational background will increase the risk of poverty and social exclusion, threatens competitiveness of workforce and thus lead to higher inequalities in societies. While school dropouts have been thoroughly studied in vocational education, its reasons are more uncharted at upper secondary school level (i.e. grades 10-12). However, at this level there are almost 3000 students each year, who either drop their studies or who are forced into year repetition.
To predict and prevent dropouts in a maintainable way, Praxis offers a two-part solution that combines big data analysis with the knowledge of behavioural economics.
1. Big data analysis and creation of an early warning system that uses live data from study information systems (SIS). As SIS is upgraded daily with grades, absences and various comments from teachers that offers an opportunity to create an IT solution working in real time (i.e. early warning system called Alarm bell). As study analytics enables to find factors and patterns that are associated with dropout, early warning system uses that knowledge to help teacher and support specialists to identify students at risk.
2. To work out a targeted solution, we will apply behavioural economics, which integrate insights from psychological research into economic science, especially what regards to human judgment. This means we will use risk patterns emerging from data analysis to create interventions aimed at individual students and that will be applied either by teachers or support specialists. In other words – we will use nudging techniques to influence behaviour. There are various examples in education, where nudging has already been applied to influence dropouts (see for example Dinkelman & Martínez 2014).
While both early warning systems and nudging have been used to influence dropouts, they have not yet been combined on a similar scale proposed here.
The potential impact of the intervention will be studied with feasibility randomized controlled trial (RCT). RCT is essentially the gold standard of scientific experiment, that aims to reduce sources of bias when testing the effectiveness of intervention.
First and second year results will include extensive big data analysis, interviews, literature review and first design of early warning system. Second and third year will show the results of the piloting of the nudges and the effectiveness of intervention (i.e Alarm bell-tool). Based on the results on students’ study outcomes and behaviour, we will either expand intervention schools – thus helping more and more students – or adapt our intervention.