What about then - Preventing drop out

<< Background >>The dropout rate in vocational education and training (VET) has been a persistent challenge in Europe for decades. Despite numerous projects and studies, dropout prevention methods have largely remained local or national. To address this, we needed innovative solutions to identify students at higher risk of dropping out and develop low-threshold services to support their well-being and commitment. The COVID-19 pandemic highlighted the need for digital solutions to provide continuous support to students, making it essential to integrate digital environments into guidance and counselling services. The use of artificial intelligence is nothing new but using it in such a concrete and practical way in educational institutions could offer an opportunity to support students and allocate human resources preventively and effectively. The participants of the What about then - project have been active in this field and wanted to learn more about the topic. However, a mere system is not enough, functional and innovative ways of acting are also needed when an alarm about the increased risk of a student dropping out appears.<< Objectives >>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.<< Implementation >>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 methodsWP3 ‘Research for innovative ways to predict dropout’ had four activities that were implemented:1)AI tool training course2)AI tool testing3)AI tool implementation4)Recommendations for using the AI toolWP4 ‘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 languages2)Survey analysis3)National workshop in partner countries4)National workshop in partner countries for students5)Designing and publishing the manual of best practisesWP5 ‘Student dropout predicting and preventing framework’ had three activities that were implemented:1)Organisation of international conference in TTK UAS2)Development of framework to predict and prevent dropout3)Organisation of the trainings at each partner institutionIn addition, we’ve organised 3 transnational meetings.<< Results >>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