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Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.
Act before it happens: Preventing and fighting Early Leaving from Education and Training with Artificial Intelligence
Project number: 2023-1-IS01-KA220-SCH-000157308
Start
WP2: T2.3
www.stai-edu.eu
Author: ScuolAttiva Onlus
Transnational map
Mapping the main risk factors of ELET
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ELET Risk Factors: Other Risk Factors
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Contents
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1) National Scope Overview 2) ELET Risk Factors3) Current Trends, Implications, and Perspectives in Addressing ELET4) Results from Interviews Teachers and School Staff5) Policymakers’ Perspectives: Structural Challenges and Long-Term Solutions6) Shared Themes and Diverging Priorities7) The Role of Technology in Reducing Dropout Rates8) Conclusion
- Identify shared and country-specific ELET challenges.
- Guide effective policies to reduce ELET.
Aim:
4. Romania 5. Portugal 6. Belgium
- Italy
- Iceland
- Cyprus
Partecipants: 6 European Countries
- Socio-demographic factors
- Insitutional afctors
- Community factors
Causes
- It's the phenomenon of young people leaving education or training prematurely, impacting their future prospects and societal well-being.
- Significant implications for social equity, economic growth, and individual well-being
What is ELET ?
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1. ELET National Scope Overview
1. ELET National Scope Overview
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ELET rates across partner countries and the EU average.
1. ELET National Scope Overview
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Gender Disparities:
- Boys show higher dropout rates than girls across all partner countries.
- Example: In Italy, 20% for boys vs. 13% for girls.
- Students from low-income families face challenges like financial constraints, limited access to education, and reduced parental involvement.
- Example: In Romania, children with parents at ISCED 0-2 level face a 52% risk of poverty, influencing dropout rates.
- Foreign-born students often face higher dropout rates compared to native students.
- Example: In Italy, migrant dropout rate is 34% vs. 14.8% for natives.
- Similar trends observed in Roma populations (Romania).
2.1 Socio-Demographic Factors
2. ELET Risk Factors
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Challenges Across Institutions:
- Overcrowded classrooms, underfunded schools, and disparities in teacher qualifications are common issues.
- Southern Italy and rural Romania face particularly inadequate educational infrastructure.
- Variability in policies impacts ELET reduction.
- Vocational and alternative education pathways show potential in lowering dropout rates.
2.2 Institutional Factors
2. ELET Risk Factors
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Key Risk Factors:
- Economic hardships and cultural attitudes toward education impact dropout rates.
- Teacher-student relationship strain exacerbates risks.
- Major barrier, especially during COVID-19 lockdowns.
- 12.3% of young people lacked access to computers during lockdowns.
- In southern regions, this rose to 20%, and 57% had no personal computers at all.
- Disparities in access highlight the urgent need for equitable technological solutions.
2.2 Community and societal factors
2. ELET Risk Factors
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Innovative Strategies:
- Italy: The "Fuoriclasse" project targets educational poverty in disadvantaged areas through motivational workshops and continuous assessment.
- Portugal: Investments in vocational training align education with career aspirations, reducing dropout rates.
- Belgium: Dual learning tracks blend academic education with practical training to prepare students for the workforce.
- Fragmented implementation and weak systemic coordination.
- Insufficient funding limits impact, particularly in Cyprus and Romania.
- COVID-19 exacerbated learning losses and disengagement among marginalized populations.
3. Current Trends, Implications, and Perspectives in Addressing ELET
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3. Current Trends, Implications, and Perspectives in Addressing ELET
Technology and Data Analytics:
- Iceland: Centralized student database streamlines monitoring.
- Romania: MATE early warning framework helps detect at-risk students.
- Italy’s NRRP focuses on:
- Increasing kindergarten placements and reducing class sizes.
- Enhancing digital resources and STEM education.
- Strengthening teacher training and digital infrastructure to ensure equitable access.
- Belgium and Portugal: Align vocational programs with labor market needs, reducing dropout rates.
- Emphasis on cohesive and adaptive systems that tackle ELET’s root causes and promote equitable education across Europe.
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Iceland:
- Mental health issues (e.g., anxiety, depression) drive disengagement.
- Solutions include project-based learning and parent-teacher communication.
- Teachers face challenges integrating asylum-seeking students with cultural and linguistic barriers.
- Male students are particularly vulnerable to early school leaving.
- Urban-rural disparities and cultural stigmas (e.g., Roma students) complicate retention efforts.
- Teachers emphasize early warning systems and remedial support.
- Calls for modernizing teaching practices and aligning assessments with societal changes.
- Challenges include bureaucratic hurdles, insufficient funding, and digital distractions.
Key Observations:
- Teachers address socio-economic hardship, mental health issues, and systemic challenges affecting early school leaving.
- Gender disparities, particularly among male students, remain a significant concern.
- Financial pressures hinder student engagement.
- Vocational pathways offer an alternative for students struggling with traditional curricula.
- Large class sizes and limited resources challenge personalized teaching.
- Teachers advocate for professional development and collaboration.
4. Results from Interviews Teachers and School Staff
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5. Policymakers’ Perspectives: Structural Challenges and Long-Term Solutions
Iceland:
- Develops a centralized data system (Frigg) to coordinate interventions and improve inclusivity.
- Seeks to address systemic resource gaps and insufficient teacher training.
- Targets urban-rural disparities and immigrant student integration.
- Leverages EU-funded initiatives and data analytics for policy improvements.
- Early Warning System (MATE) addresses risks for vulnerable groups, including Roma students.
- Faces significant financial constraints limiting the reach of these interventions.
- Uses AI and databases like INVALSI to personalize learning and track at-risk students.
- Stresses collaboration among schools, authorities, and communities while acknowledging challenges such as funding and teacher training.
Key Structural Challenges:Common Issues Across Countries:
- Equity and access remain critical concerns, particularly in rural or underserved areas.
- Financial and resource constraints limit the implementation of effective policies.
- Prioritizes data-driven policies to monitor at-risk students and expand vocational programs.
- Emphasizes mental health support as part of dropout reduction strategies.
- Focuses on equitable access to education for marginalized groups.
- Explores AI’s potential for monitoring and predicting dropout risks.
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www.stai-edu.eu
Common Themes Across Six Countries:Both teachers and policymakers emphasize the impact of:
- Socio-economic inequalities.
- Mental health challenges.
- Systemic barriers to student retention.
- Teachers:
- Prioritize immediate, classroom-based interventions.
- Focus on adapting teaching practices, fostering strong teacher-student relationships, and addressing day-to-day challenges.
- Policymakers:
- Emphasize long-term structural changes.
- Focus on equity-focused policies, resource allocation, and integrating advanced technologies (e.g., Italy’s dual perspective).
- Teachers advocate for modernized assessments and intrinsic motivation strategies.
- Policymakers stress addressing socio-economic disparities and enhancing inter-institutional collaboration.
- Both groups agree on the need for classroom innovation and systemic reform for effective solutions.
6) Shared Themes and Diverging Priorities
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Technology as a Key Tool:Policymakers and teachers highlight the potential of:
- Predictive analytics.
- Artificial intelligence (AI).
- Centralized data systems for identifying at-risk students and tailoring interventions.
- Iceland: "Frigg" platform enhances student tracking and coordination.
- Romania: MATE early warning system aids in identifying vulnerable students.
- Italy: INVALSI data use demonstrates the potential of data-driven personalization in education.
- Technology alone cannot address ELET.
- Requires adequate funding, teacher training, and integration with traditional practices.
7) The Role of Technology in Reducing Dropout Rates
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Successes and Challenges:
- Successes:
- Belgium & Portugal: Vocational pathways.
- Italy: "Fuoriclasse" program.
- Iceland: Mental health initiatives.
- Challenges:
- Persistent disparities in Italy, Romania, and Cyprus.
- Reducing ELET needs classroom innovation, systemic reform, and community engagement to ensure equitable opportunities for all students.
Key Insights:
- Addressing Early Leaving from Education and Training (ELET) requires:
- Immediate interventions by teachers.
- Long-term reforms driven by policymakers.
- Focus on building strong student relationships.
- Address socio-economic inequalities, mental health challenges, and cultural barriers.
- Emphasize context-sensitive, targeted solutions.
- Drive equity-focused reforms and resource allocation.
- Leverage technology like:
- Iceland: "Frigg" system.
- Romania: MATE framework.
- Italy: INVALSI data.
- Advocate for systemic collaboration and teacher training.
8) Conclusions
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www.stai-edu.eu
Transnational map
Mapping the main risk factors of ELET
Project number: 2023-1-IS01-KA220-SCH-000157308
• Integration of Asylum Seekers: Cyprus, with one of the highest asylum application-to-inhabitant ratios in the EU, faces considerable challenges in integrating asylum seekers, many of whom are youth aged 18-34. • Data Collection Issues: Reporting primarily relies on a school-by-school approach, making it difficult to form comprehensive datasets on ELET. • Support Measures: Targeted support for migrant students is provided through initiatives like the DRASE+ project, which is backed by the European Social Fund (ESF+). Moreover, the Ministry of Education helps teachers manage linguistic and cultural diversity in schools.
Key Trends and Observations:
• The rise in early leaving from education and training (ELET) since 2015 is particularly notable among foreign-born youth, with the rate of early leaving significantly higher than the EU average (27.9% vs. 21.5%). • In contrast, native-born youth show much lower rates, at 3.9%, which is well below the EU average of 8.5%.
Key Trends and Observations:
ELET Percentages
General Data: • Overall Percentage: 27.9% (2021) Gender Distribution: • Males: Approximately two-thirds of early leavers are male. • Females: One-third of early leavers are female. Age Group Distribution: • 18-34 years: Among recently arrived asylum seekers, 62% fall within this age group. Regional Distribution: • Urban Areas: 10.7% of early leavers (2021), showing a significant increase from 3.8% in 2015.
Flexible secondary education pathways for students who have not completed compulsory schooling.
Highlights
- ELET rate: 16% (higher than the EU average of 9.5%).
- NEET rate: <5% (one of the lowest in Europe).
ELET Percentages
Challenges
- High dropout rates in rural areas and among marginalized groups, especially the Roma population.
- Poverty and lack of infrastructure exacerbating risks.
ELET Percentages
ELET rate: 15.6% (declining trend but far from the 2020 target of 11.3%).
Italy's Early School Leaving Challenge
Italy's rate of early school leaving is significantly above the European Union average of 9.7%. The challenge is more pronounced in southern regions where socioeconomic disparities exacerbate the issue.
ELET Percentages
General Data:• Overall Percentage: 12.7% of young people aged 18-24 leave school early at the national level. Gender Distribution: - Males: 20% dropout rate. - Females: 13% dropout rate. Age Group Distribution: -Data focuses primarily on ages 18-24. Regional Distribution: -Southern regions: 16.6% dropout rate. -National average: 12.7%.
- Economic inequality.
- Rural-urban disparities.
Challenges
ELET Percentages
8% (recent increase despite long-term reductions).
Dual Education System
• Flanders' education system divides secondary students into academic (ASO), arts (KSO), technical (TSO), and vocational (BSO) tracks. • Institutionalized certificates (A, B, C) determine students’ progression. • Dual learning programs, combining school and work, were introduced in 2019 as a reform to address educational needs
ELET Percentages
Overall Percentage: - 6.4% of the population aged 18-24 left school early. Age Group Distribution:- Early school leaving increases with age, influenced by family responsibilities and the need to enter the labor market. Regional Distribution: - Flanders: Lower rates than other Belgian regions.- Urban Areas (e.g., Antwerp and Ghent): Higher dropout rates, especially among lower socioeconomic and ethnic minority groups.