Whole Lotta Training - Studying School-to-Training Transitions by Training Artificial Neural Networks - Dennis Oliver Kubitza; Katarina Weßling (ZBW - Leibniz Information Centre for Economics, 2025)
Transitions from school to further education, training, or work are among the most extensively researched topics in the social sciences. Success in such transitions is influenced by predictors operating at multiple levels, such as the individual, the institutional, or the regional level. These levels are intertwined, creating complex inter-dependencies in their influence on transitions. To unravel them, researchers typically apply (multilevel) regression techniques and focus on mediating and moderating relations between distinct predictors. Recent research demonstrates that machine learning techniques can uncover previously overlooked patterns among variables. To detect new patterns in transitions from school to vocational training, we apply artificial neural networks (ANNs) trained on survey data from the German National Educational Panel Study (NEPS) linked with regional data. For an accessible interpretation of complex patterns, we use explainable artificial intelligence (XAI) methods. We establish multiple non-linear interactions within and across levels, concluding that they have the potential to inspire new substantive research questions. We argue that adopting ANNs in the social sciences yields new insights into established relationships and makes complex patterns more accessible
Keywords: 370; school-to-work transitions; VET; machine learning; explainable artificial neuronal networks; SHAP values; rule extraction