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Regional Government of Andalusia
The Regional Ministry of Education and Vocational Training in Andalusia needed a way to predict key factors impacting educational management. Ultimately, the aim was to improve the impact and responsiveness of the services provided by the General Directorate of Planning and Vocational Training.
In terms of impact, this involved modelling student progress, the likelihood of vocational students completing their studies and the probability of students dropping out. The goal was to enable timely, tailored support, such as extra lessons or careers guidance, to be offered to pupils.
Regarding responsiveness, the modelling of processes governing teams and departments within schools aimed to provide accurate predictions of staffing needs based on likely enrolment.
In order to meet these needs, a joint venture comprised of Ayesa and Datrik developed HipatIA, a tool that provides predictive information as well as data analysis and processing through AI.
The project, financed by the European Regional Development Fund (ERDF), has essentially harnessed the power of predictive modelling to improve the services offered by Andalusia’s Ministry of Education and Vocational Training.
HipatIA is a continuation of the Séneca application, created by the Regional Government of Andalusia and Ayesa. Its purpose was to allow schools as well as educational support services, inspections, programmes and activities in Andalusia to be effectively managed. In fact, the data used by HipatIA to model the various processes has come from Séneca.
The solution, which has been a resounding success, has developed a number of models able to predict key aspects of educational management. These include the number of teaching staff required at schools, the number of pupils in each year, the likelihood of students dropping out, and the trajectories of pupils. As a result, Andalusia is now able to manage its educational services and resources much more effectively and efficiently.
HipatIA has brought with it a whole host of benefits.
Firstly, it allows the progress of pupils to be modelled from primary school through to sixth form, thereby allowing schools to plan ahead in terms of educational needs.
Secondly, it enables the number of teaching vacancies to be adjusted for each year, subject and school, ensuring proper staffing.
Thirdly, it can predict the likelihood of pupils dropping out of primary and secondary school, allowing staff to take proactive measures and reduce the risk.
And finally, it has the capacity to predict the likelihood of students in vocational training completing their studies, based on the different types of courses available.
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