Enhancing learning at work. How to combine theoretical and data-driven approaches, and multiple levels of data?
Verleysen, M. (editor)
This research plan focuses on learning at work. Our aim is to gather empirical data on multiple factors that can affect learning for work, and to apply computational methods in order to understand the preconditions of effective learning. The design will systematically combine theory- and data-driven approaches to study (i) whether principles of effective learning found in previous studies apply to real life settings, (ii) what interactions between individual and organizational factors are related to learning outcomes, and (iii) new connections and phenomena relevant to enhance learning in real life.
Urban Mobility & Environment
To reference this document use:
UES - Urban Environment & Safety
ELSS - Earth, Life and Social Sciences
Work and Employment
Proceedings of the ESANN 2015, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 331-336