STudent’s Academic perfoRmance: a machine learning APProach for risk assessment and drop out prevention Erasmus Project

Project Title

STudent’s Academic perfoRmance: a machine learning APProach for risk assessment and drop out prevention

Project Key Action

This project related with these key action: Cooperation for innovation and the exchange of good practices

Project Action Type

This project related with this action type : Strategic Partnerships for higher education

Project Call Year

This project’s Call Year is 2020

Project Topics

This project is related with these Project Topics: Research and innovation; Early School Leaving / combating failure in education; ICT – new technologies – digital competences

Project Summary

The cooperation will explore the potentials of Artificial Intelligence (AI) in the field of Higher Education with the aim of decreasing academic dropout and increasing academic attainment across European countries.
The project will use AI to create a system that will be able to preventively detect academic students who are at risk for academic dropout. Using machine learning algorithms, the project will be able to define profiles of students at risk at the beginning of the academic year and, with a co-design approach, will then develop new preventive interventions specifically targeting the students at risk of dropout (during the first year of university).

The project will result in a new integrated system for academic tutoring services who would then be able to:
– preventively detect students at risk of dropout.
– profiling the different students at risk based on the specific issues they might be facing.
– offer personalized services and tools that match the students’ needs.
– offer training to tutors and academic institutions to enable them to optimally use the system and the services created within the project.

The consortium will base this project on previous literature on academic dropout and on some relevant existing initiatives and best experiences in the field of digital technologies for career guidance (based on the results of the Etutoring project, LEADER project and MYFUTURE project). The project will also promote a wide and open use of digital tools as recommended in the 2013 EU Communication on Opening Up Education.
The main innovative points are:
• The use of AI techniques in the field of education.
• The use of AI with a preventive approach.
• A preventive approach in the field of higher education to optimize resources and services.
• The direct involvement of researchers, experts, career guidance practitioners, students and tutors for a joint design and implementation of innovative interventions to help students in their academic journey.
• The use of co-design techniques and design thinking methodologies.
• The creation of open resources for supporting interventions to prevent academic dropouts.

The intellectual outputs will be:
– Toolkit for tutors and practitioners: reference framework for academic career guidance practitioners;
– STAR.APP online platform for risk assessment: matching the future for your success;
– Handbook: New frontiers for preventing academic dropout.

EU Grant (Eur)

Funding of the project from EU: 235095 Eur

Project Coordinator

UNIVERSIDAD DE ZARAGOZA & Country: ES

Project Partners

• CENTRO STUDI PLURIVERSUM SRL
• UNIVERZA V MARIBORU
• UNIVERSITA DEGLI STUDI DI CAMERINO
• EDEX – EDUCATIONAL EXCELLENCE CORPORATION LIMITED
• VIESOJI ISTAIGA SOCIALINIU MOKSLU KOLEGIJA