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Whole Lotta Training - Studying School-to-Training Transitions by Training Artificial Neural Networks

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  • Kubitza, Dennis Oliver
  • Weßling, Katarina
Abstract
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

Suggested Citation

  • Kubitza, Dennis Oliver & Weßling, Katarina, 2025. "Whole Lotta Training - Studying School-to-Training Transitions by Training Artificial Neural Networks," EconStor Preprints 310974, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esprep:310974
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    File URL: https://www.econstor.eu/bitstream/10419/310974/1/Whole-Lotta-Training.pdf
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    References listed on IDEAS

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    1. Cindi Mason & Janet Twomey & David Wright & Lawrence Whitman, 2018. "Predicting Engineering Student Attrition Risk Using a Probabilistic Neural Network and Comparing Results with a Backpropagation Neural Network and Logistic Regression," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(3), pages 382-400, May.
    2. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    3. Micklewright, John & Pearson, Mark & Smith, Stephen, 1990. "Unemployment and Early School Leaving," Economic Journal, Royal Economic Society, vol. 100(400), pages 163-169, Supplemen.
    4. Simone R Haasler, 2020. "The German system of vocational education and training: challenges of gender, academisation and the integration of low-achieving youth," Transfer: European Review of Labour and Research, , vol. 26(1), pages 57-71, February.
    5. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
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