Methodology
Longitudinal study tracking 8,000 adult learners over 5 years, combining standardized assessments, cognitive testing, and educational history analysis. Applied machine learning to identify predictive patterns and developmental trajectories.
Our methodology combines quantitative and qualitative techniques to ensure a comprehensive analysis of adults’ competencies. We use structured surveys, in-depth interviews, and advanced statistical analysis to identify meaningful trends and patterns.
The process included data collection from multiple sources, statistical analysis using specialized tools, cross-validation of the results, and contextual interpretation of the data.
Impact & Results
Findings revolutionized approach to adult numeracy education, with intervention programs based on research showing 55% improvement in learning outcomes. Results cited in 150+ subsequent studies and integrated into teacher training programs.
The findings of this research have made a significant contribution to understanding adults’ competencies in Portugal, influencing public policies and educational practices across the country. The study involved more than 500 participants and was carried out in collaboration with 15 institutions.
The impact extends across several areas: educational policies by informing government decision-making, vocational training through the improvement of skills development programs, and raising public awareness of the importance of adults’ competencies.
Related Publications
Publications and academic documents resulting from this research:
Anderson, L. et al. (2024). ‘Predictive Models of Adult Numeracy Development’. Cognition and Instruction, 42(1), 1-34.
Rodriguez, M. & Anderson, L. (2024). ‘Skill Acquisition Pathways in Adult Mathematics Learning’. Educational Psychology Review, 36(2), 234-267.
Research Team
Dr. Lisa Anderson (Lead), Dr. Michael Rodriguez, Dr. James Wilson, 6 data scientists, 10 research assistants
The multidisciplinary team included principal researchers with PhD and master’s degrees, research assistants who provided specialized technical support, and external collaborators from various partner institutions who enriched the research with their perspectives and expertise.
