Clone of Project to Promote Academic Success and Reduce Higher Education Dropouts

The programme aims to ensure the success of first-year students enrolling for the first time. The renewal rate at the end of the enrolled academic year and the increase in the average number of university credits achieved will work as outcome indicators. Furthermore, the programmes to counter academic failure and academic dropout through tutoring and mentoring will receive considerable consideration, as will the monitoring mechanisms by teachers and peers.

ULisboa’s proposal, which was awarded the maximum score by the evaluation panel, forecasts the implementation of a set of 16 actions organised into four groups, to be executed by 2024:
 

Students

The first group concerns the student body. It aims to support students’ integration into higher education, decreasing the risk of academic failure and subsequent dropout.

• It includes:encouraging mentoring and tutoring systems;
• supporting students’ integration and adaptation to higher education;
• assembling teams to facilitate students’ integration into university residences;
• supporting students with specific educational needs;
• supporting students from Portuguese-speaking countries;
• creating online study and collaborative working spaces.
   

Teaching Staff

The second group includes activities addressing the teaching staff, enabling them to adopt effective, active and innovative teaching methodologies to improve the teaching-learning process. This group includes:

• the launching of the ULTRA programme – ULisboa in the Transformation and Restructuring of Academic learning;
• regular pedagogical training cycles for teachers;
• the Pedagogical Training Programme for Early Career Teachers;
• the Pedagogical Consultancy Programme;
• the development and accessibility of a pedagogical "toolbox;"
• a call for competition for pedagogical innovation projects.

Management Tools

The third group consists of activities that provide for the development of management tools capable of facilitating the detection and alerting of dropout risk situations. This group includes:

• the creation of alerts in the Fénix academic management system; 
• the use of Data Science and Machine Learning algorithms to identify risk profiles and anticipate cases of academic failure and dropout;
• the development of tools for tracking workload throughout the semester.

Best Practices

The fourth group consists of general activities, aiming to create conditions to increase the sharing of best practices among all ULisboa Schools.