What about then - Preventing drop out

The project had three primary objectives: 1. Innovative digital tools: Develop tools to predict dropout and early school leaving by identifying risks and indicators. Using artificial intelligence...

The project had three primary objectives: 1. Innovative digital tools: Develop tools to predict dropout and early school leaving by identifying risks and indicators. Using artificial intelligence (AI) to help allocate guidance, counselling, and social services proactively. 2. Preventive measures: Implement new methods to act on identified risks at an early stage. These services and tools will be available to students regardless of time and place, ensuring continuous support. 3. Active student participation: Engage students in quality assurance processes, allowing them to evaluate and provide feedback on new services. This approach ensures that even typically non-participating students have a voice in the development process.

The project was implemented through work packages (WPs): WP2 ‘Study of reasons for dropout’ had four activities that were implemented: 1) Literature Review Report. Categories of reasons for student dropouts. 2) Report Dropouts statistics from each partner country. Analysing and summarising findings. 3) Study methodology and methods WP3 ‘Research for innovative ways to predict dropout’ had four activities that were implemented: 1) AI tool training course 2) AI tool testing 3) AI tool implementation 4) Recommendations for using the AI tool WP4 ‘Study for best practices in preventing dropout’ we had five activities that were implemented: 1) Survey for best practices in preventing dropout on European level in five different languages 2) Survey analysis 3) National workshop in partner countries 4) National workshop in partner countries for students 5) Designing and publishing the manual of best practises WP5 ‘Student dropout predicting and preventing framework’ had three activities that were implemented: 1) Organisation of international conference in TTK UAS 2) Development of framework to predict and prevent dropout 3) Organisation of the trainings at each partner institution In addition, we’ve organised 3 transnational meetings.

The project achieved significant outcomes: WP2: Produced a comprehensive report on dropout reasons and statistics. WP3: Held an AI tool training course, increasing competence among partner organizations, and published recommendations for using the AI tool. WP4: Conducted surveys and workshops, resulting in the publication of a manual of best practices. WP5: Organised an international conference and developed an evidence-based framework to predict and prevent dropout, with training materials accessible online. All results are available here https://ysao.fi/2022/10/06/what-about-then-wat-opinnoissa-pysyvyyden-kehittamishanke/ and here https://sites.google.com/tktk.ee/wat-preventing-dropout-project/wat-home